# nxalg

This module, named **nxalg**, provides a comprehensive set of thin wrappers
around most of the algorithms present in the NetworkX (opens in a new tab)
package. The wrapper functions have the capability to create a NetworkX
compatible graph-like object that can stream the native database graph directly
saving on memory usage significantly.

Trait | Value |
---|---|

Module type | module |

Implementation | Python |

Graph direction | directed/undirected |

Edge weights | weighted/unweighted |

Parallelism | sequential |

If you are not satisfied with the performance of algorithms from the `nxalg`

module, check Memgraph's native implementation of algorithms such as
PageRank, betweenness
centrality,
and others written in C++.

## Procedures

You can execute this algorithm on graph projections, subgraphs or portions of the graph.

`all_shortest_paths()`

Compute all simple shortest paths in the graph. A simple path is a path with no repeated nodes.

#### Input:

`source: Vertex`

➡ Starting node for the path.`target: Vertex`

➡ Ending node for the path.`weight: string (default=NULL)`

➡ If`NULL`

, every relationship has weight/distance/cost 1. If a`string`

, use this relationship property as the relationship weight. Any relationship property not present defaults to 1.`method: string (default="dijkstra")`

➡ The algorithm used to compute the path lengths. Supported options:`dijkstra`

,`bellman-ford`

. Other inputs produce a`ValueError`

. If`weight`

is`None`

, unweighted graph methods are used, and this suggestion is ignored.

#### Output:

`paths: List[Vertex]`

➡ List of ndoes for a certain path.

#### Usage:

To find all shortest paths, use the following query:

```
MATCH (n:Label), (m:Label)
CALL nxalg.all_shortest_paths(n, m)
YIELD paths
RETURN paths;
```

`all_simple_paths()`

Returns all simple paths in the graph `G`

from source to target. A simple path
is a path with no repeated nodes.

#### Input:

`source: Vertex`

➡ Starting node for the path.`target: Vertex`

➡ Ending node for the path.`cutoff: List[integer] (default=NULL)`

➡ Depth to stop the search. Only paths of`length <= cutoff`

are returned.

#### Output:

`paths: List[Vertex]`

➡ List of nodes of a certain path. If there are no paths between the source and target within the given cutoff there is no output.

#### Usage:

To find all simple paths, use the following query:

```
MATCH (n:Label), (m:Label)
CALL nxalg.all_simple_paths(n, m, 5)
YIELD paths
RETURN paths;
```

`ancestors()`

Returns all nodes having a path to `source`

in `G`

.

#### Input:

`source: Vertex`

➡ Starting node. Calculates all nodes that have a path to`source`

.

#### Output:

`ancestors: List[Vertex]`

➡ List of vertices that have a path toward source node.

#### Usage:

To find ancestors, use the following query:

```
MATCH (n:Label)
CALL nxalg.ancestors(n)
YIELD ancestors
RETURN ancestors;
```

`betweenness_centrality()`

Compute the shortest-path betweenness centrality for nodes. *Betweenness
centrality* is a measure of centrality in a graph based on shortest paths.
Centrality identifies the most important nodes within a graph.

#### Input:

`k: string (default=NULL)`

➡ If`k`

is not`None`

, use`k`

node samples to estimate betweenness. The value of`k <= n`

where`n`

is the number of nodes in the graph. Higher values give a better approximation.`normalized: boolean (default=True)`

➡ If`True`

the betweenness values are normalized by`2/((n-1)(n-2))`

for graphs, and`1/((n-1)(n-2))`

for directed graphs where`n`

is the number of nodes in`G`

.`weight: string (default=NULL)`

➡ If`None`

, all relationship weights are considered equal. Otherwise holds the name of the relationship attribute used as weight.`endpoints: boolean (default=False)`

➡ If`True`

, includes the endpoints in the shortest path counts.`seed: integer (default=NULL)`

➡ Indicator of random number generation state. Note that this is only used if`k`

is not`None`

.

#### Output:

`node: Vertex`

➡ Graph node for betweenness calculation.`betweenness: double`

➡ Value of betweenness for a given node.

#### Usage:

To calculate betweenness centrality, use the following query:

```
CALL nxalg.betweenness_centrality(20, True)
YIELD node, betweenness
RETURN node, betweenness;
```

`bfs_edges()`

Iterate over relationships in a breadth-first-search starting at source.

#### Input:

`source: Vertex`

➡ Specify starting node for breadth-first search. This function iterates only over relationships in the component that are reachable from this node.`reverse: boolean (default=False)`

➡ If`True`

, traverse a directed graph in the reverse direction.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`edges: List[Edge]`

➡ List of relationships in the breadth-first search.

#### Usage:

To iterate over relationships in a breadth-first-search, use the following query:

```
MATCH (n:Label)
CALL nxalg.bfs_edges(n, False)
YIELD edges
RETURN edges;
```

`bfs_predecessors()`

Returns an iterator of predecessors in breadth-first-search from source.

#### Input:

`source: Vertex`

➡ Specify starting node for breadth-first search.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`node: Vertex`

➡ Node in a graph.`predecessors: List[Vertex]`

➡ List of predecessors of given node.

#### Usage:

To find the iterator of predecessorss, run the following query:

```
MATCH (n:Label)
CALL nxalg.bfs_predecessors(n, 10)
YIELD node, predecessors
RETURN node, predecessors;
```

`bfs_successors()`

Returns an iterator of successors in breadth-first-search from source.

#### Input:

`source: Vertex`

➡ Specify starting node for breadth-first search.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`node: Vertex`

➡ Node in a graph.`successors: List[Vertex]`

➡ List of successors of given node.

#### Usage:

To find the iterator of successors, run the following query:

```
MATCH (n:Label)
CALL nxalg.bfs_successors(n, 5)
YIELD node, successors
RETURN node, successors;
```

`bfs_tree()`

Returns an oriented tree constructed from of a breadth-first-search starting at
`source`

.

#### Input:

`source: Vertex`

➡ Specify starting node for breadth-first search.`reversed: boolean (default=False)`

➡ If`True`

, traverse a directed graph in the reverse direction.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`tree: List[Vertex]`

➡ An oriented tree in a list format.

#### Usage:

To get an oriented tree, run the following query:

```
MATCH (n:Label)
CALL nxalg.bfs_tree(n, True, 3)
YIELD tree
RETURN n, tree;
```

`biconnected_components()`

Returns a list of sets of nodes, one set for each biconnected component of the graph

*Biconnected components* are maximal subgraphs such that the removal of a
node (and all relationships incident on that node) will not disconnect the
subgraph. Note that nodes may be part of more than one biconnected
component. Those nodes are articulation points or cut vertices. The
removal of articulation points will increase the number of connected
components of the graph.

Notice that by convention a dyad is considered a biconnected component.

#### Output:

`components: List[List[Vertex]]`

➡ A list of sets of nodes, one set for each biconnected component.

#### Usage:

To find biconnected components, run the following query:

```
CALL nxalg.biconnected_components()
YIELD components
RETURN components;
```

`bridges()`

Returns all bridges in a graph.

A *bridge* in a graph is a relationship which when removed causes the number of
connected components of the graph to increase. Equivalently, a bridge is an
relationship that does not belong to any cycle.

#### Input:

`root: Vertex (default=NULL)`

➡ A node in the graph`G`

. If specified, only the bridges in the connected components containing this node will be returned.

#### Output:

`bridges: List[Edge]`

➡ A list of relationships in the graph which when removed disconnects the graph (or causes the number of connected components to increase).

#### Usage:

To find all bridges in a graph:

```
CALL nxalg.bridges()
YIELD bridges
RETURN bridges;
```

`center()`

Returns the center of the graph `G`

.

The *center* is the set of nodes with eccentricity equal to the radius.

#### Output:

`center: List[Vertex]`

➡ List of nodes in center.

#### Usage:

To find the center of the graph, run the following query:

```
CALL nxalg.center()
YIELD center
RETURN center;
```

`chain_decomposition()`

Returns the chain decomposition of a graph.

The *chain decomposition* of a graph with respect to a depth-first search tree
is a set of cycles or paths derived from the set of fundamental cycles of the
tree in the following manner. Consider each fundamental cycle with respect to
the given tree, represented as a list of relationships beginning with the
non-tree relationship oriented away from the root of the tree. For each
fundamental cycle, if it overlaps with any previous fundamental cycle, just take
the initial non-overlapping segment, which is a path instead of a cycle. Each
cycle or path is called a *chain*.

#### Input:

`root: Vertex[default=NULL]`

➡ Optional. A node in the graph`G`

. If specified, only the chain decomposition for the connected component containing this node will be returned. This node indicates the root of the depth-first Search tree.

#### Output:

`chains: List[List[Edge]]`

➡ A list of relationships representing a chain. There is no guarantee on the orientation of the relationships in each chain (for example, if a chain includes the relationship joining nodes 1 and 2, the chain may include either (1, 2) or (2, 1)).

#### Usage:

To get the chain decomposition of a graph, run the following query:

```
MATCH (n:Label)
CALL nxalg.chain_decomposition(n)
YIELD chains
RETURN chains;
```

`check_planarity()`

Check if a graph is planar.

A graph is planar if it can be drawn in a plane without any relationship intersections.

#### Output:

`is_planar: boolean`

➡`True`

if the graph is planar.

#### Usage:

To check if the graph is planar, run the following query:

```
CALL nxalg.check_planarity()
YIELD is_planar
RETURN is_planar;
```

`clustering()`

Compute the clustering coefficient for nodes.

A *clustering coefficient* is a measure of the degree to which nodes
in a graph tend to cluster together.

#### Input:

`nodes: List[Vertex] (default=NULL)`

➡ Compute clustering for nodes in this container.`weight: string (default=NULL)`

➡ The relationship attribute that holds the numerical value used as a weight. If`None`

, then each relationship has weight 1.

#### Output:

`node: Vertex`

➡ Node in graph for calculation of clustering.`clustering: double`

➡ Clustering coefficient at specified nodes.

#### Usage:

To compute the clustering coefficient, run the following query:

```
MATCH (n:SpecificLabel)
WITH COLLECT(n) AS cluster_nodes
CALL nxalg.clustering(cluster_nodes)
YIELD node, clustering
RETURN node, clustering;
```

`communicability()`

Returns communicability between all pairs of nodes in `G`

.

The *communicability* between pairs of nodes in `G`

is the sum of
closed walks of different lengths starting at node `u`

and ending at node `v`

.

#### Output:

`node1: Vertex`

➡ The first value in communicability calculation.`node2: Vertex`

➡ The second value in communicability calculation.`communicability: double`

➡ The value of communicability between two values.

#### Usage:

To calculate communicability, run the following query:

```
CALL nxalg.communicability()
YIELD node1, node2, communicability
RETURN node1, node2, communicability
ORDER BY communicability DESC;
```

`core_number()`

Returns the core number for each node.

A *k-core* is a maximal subgraph that contains nodes of degree `k`

or more.

The core number of a node is the largest value `k`

of a k-core containing
that node.

#### Output:

`node: Vertex`

➡ Node to calculate k-core for.`core: integer`

➡ Largest value`k`

of a k-core.

#### Usage:

To calculate the core number, run the following query:

```
CALL nxalg.core_number()
YIELD node core
RETURN node, core
ORDER BY core DESC;
```

`degree_assortativity_coefficient()`

Compute degree assortativity of a graph.

*Assortativity* measures the similarity of connections
in the graph with respect to the node degree.

#### Input:

`x: string (default="out")`

➡ The degree type for source node (directed graphs only). Can be "in" or "out".`y: string (default="in")`

➡ The degree type for target node (directed graphs only). Can be "in" or "out".`weight: string (default=NULL)`

➡ The relationship attribute that holds the numerical value used as a weight. If`None`

, then each relationship has weight- The degree is the sum of the relationship weights adjacent to the node.

`nodes: List[Vertex] (default=NULL)`

➡ Compute degree assortativity only for nodes in a container. The default is all nodes.

#### Output:

`assortativity: double`

➡ Assortativity of graph by degree.

#### Usage:

To compute degree assortativity of a graph, run the following query:

```
CALL nxalg.degree_assortativity_coefficient('out', 'in')
YIELD assortativity
RETURN assortativity;
```

`descendants()`

Returns all nodes reachable from `source`

in `G`

.

#### Input:

`source: Vertex`

➡ A node in`G`

.

#### Output:

`descendants: List[Vertex]`

➡ The descendants of`source`

in`G`

.

#### Usage:

To compute degree assortativity, run the following query:

```
MATCH (source:Label)
CALL nxalg.descendants(source)
YIELD descendants
RETURN descendants;
```

`dfs_postorder_nodes()`

Returns nodes in a depth-first-search post-ordering starting at source.

#### Input:

`source: Vertex`

➡ Specify the maximum search depth.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`nodes: List[Vertex]`

➡ A list of nodes in a depth-first-search post-ordering.

#### Usage:

To return nodes in a DFS post-ordering, run the following query:

```
MATCH (source:Label)
CALL nxalg.dfs_postorder_nodes(source, 10)
YIELD nodes
RETURN source, nodes;
```

`dfs_predecessors()`

Returns a dictionary of predecessors in depth-first-search from source.

#### Input:

`source: Vertex`

➡ Specify the maximum search depth.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`node: Vertex`

➡ Node we are looking a predecessor for.`predecessor: Vertex`

➡ predecessor of a given node.

#### Usage:

To return a dictionary of predecessors, run the following query:

```
MATCH (source:Label)
CALL nxalg.dfs_predecessors(source, 10)
YIELD node, predecessor
RETURN node, predecessor;
```

`dfs_preorder_nodes()`

Returns nodes in a depth-first-search pre-ordering starting at source.

#### Input:

`source: Vertex`

➡ Specify starting node for depth-first search and return nodes in the component reachable from this node.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`nodes: List[Vertex]`

➡ A list of nodes in a depth-first-search pre-ordering.

#### Usage:

To return nodes in a DFS pre-ordering, run the following query:

```
MATCH (source:Label)
CALL nxalg.dfs_preorder_nodes(source, 10)
YIELD nodes
RETURN source, nodes AS preoder_nodes;
```

`dfs_successors()`

Returns a dictionary of successors in depth-first-search from source.

#### Input:

`source: Vertex`

➡ Specify starting node for depth-first search and return nodes in the component reachable from this node.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`node: Vertex`

➡ Node to calculate successors`successors: List[Vertex]`

➡ Successors of a given nodes

#### Usage:

To get a dictionary of successors, run the following query:

```
MATCH (source:Label)
CALL nxalg.dfs_successors(source, 5)
YIELD node, successors
RETURN node, successors;
```

`dfs_tree()`

Returns an oriented tree constructed from a depth-first-search from source.

#### Input:

`source: Vertex`

➡ Specify starting node for depth-first search.`depth_limit: integer (default=NULL)`

➡ Specify the maximum search depth.

#### Output:

`tree: List[Vertex]`

➡ An oriented tree in a form of a list.

#### Usage:

To get an oriented tree construct, run the following query:

```
MATCH (source:Label)
CALL nxalg.dfs_tree(source, 7)
YIELD tree
RETURN tree;
```

`diameter()`

Returns the diameter of the graph `G`

.

The diameter is the maximum eccentricity.

#### Output:

`diameter: integer`

➡ Diameter of graph.

#### Usage:

To get the diameter of the graph, run the following query:

```
CALL nxalg.diameter()
YIELD diameter
RETURN diameter;
```

`dominance_frontiers()`

Returns the dominance frontiers of all nodes of a directed graph.

The *dominance frontier* of a node `d`

is the set of all nodes such that `d`

dominates an immediate predecessor of a node, but `d`

does not strictly dominate
that node.

#### Input:

`start: Vertex`

➡ The start node of dominance computation.

#### Output:

`node: Vertex`

➡ Node to calculate frontier.`frontier: List[Vertex]`

➡ Dominance frontier for a given node.

#### Usage:

To calculate dominance frontiers, run the following query:

```
MATCH (source:Label)
CALL nxalg.dominance_frontiers(source)
YIELD node, frontier
RETURN node, frontier;
```

`dominating_set()`

Finds a dominating set for the graph `G`

.

A *dominating set* for a graph with node set `V`

is a subset `D`

of `V`

such
that every node not in `D`

is adjacent to at least one member of `D`

.

#### Input:

`start: Vertex`

➡ Node to use as a starting point for the algorithm.

#### Output:

`dominating_set: List[Vertex]`

➡ A dominating set for`G`

.

#### Usage:

To find a dominating set for the graph, run the following query:

```
MATCH (source:Label)
CALL nxalg.dominating_set(source)
YIELD dominating_set
RETURN dominating_set;
```

`edge_bfs()`

A directed, breadth-first-search of relationships in `G`

, beginning at `source`

.

Return the relationships of `G`

in a breadth-first-search order continuing until
all relationships are generated.

#### Input:

`source: Vertex`

➡ The node from which the traversal begins. If`None`

, then a source is chosen arbitrarily and repeatedly until all relationships from each node in the graph are searched.`orientation: string (default=NULL)`

➡ For directed graphs and directed multigraphs, relationship traversals need not respect the original orientation of the relationships. When set to`reverse`

, every relationship is traversed in the reverse direction. When set to`ignore`

, every relationship is treated as undirected. When set to`original`

, every relationship is treated as directed. In all three cases, the returned relationship tuples add a last entry to indicate the direction in which that relationship was traversed. If`orientation`

is`None`

, the returned relationship has no direction indicated. The direction is respected, but not reported.

#### Output:

`edges: List[Edges]`

➡ A directed relationship indicating the path taken by the breadth-first-search. For graphs, relationship is of the form`(u, v)`

where`u`

and`v`

are the tail and head of the relationship as determined by the traversal. For multigraphs, relationship is of the form`(u, v, key)`

, where`key`

is the key of the relationship. When the graph is directed, then u and`v`

are always in the order of the actual directed relationship. If`orientation`

is not`None`

then the relationship tuple is extended to include the direction of traversal (`forward`

or`reverse`

) on that relationship.

#### Usage:

To return the list of relationships, run the following query:

```
MATCH (source:Label)
CALL nxalg.edge_bfs(source, 'ignore')
YIELD edges
RETURN source, edges;
```

`edge_dfs()`

A directed, depth-first-search of relationships in `G`

, beginning at `source`

.

Return the relationships of `G`

in a depth-first-search order continuing until
all relationships are generated.

#### Input:

`source: Vertex (default=NULL)`

➡ The node from which the traversal begins. If`None`

, then a source is chosen arbitrarily and repeatedly until all relationships from each node in the graph are searched.`orientation: string (default=NULL)`

➡ For directed graphs and directed multigraphs, relationship traversals need not respect the original orientation of the relationships. When set to`reverse`

, every relationship is traversed in the reverse direction. When set to`ignore`

, every relationship is treated as undirected. When set to`original`

, every relationship is treated as directed. In all three cases, the returned relationship tuples add a last entry to indicate the direction in which that relationship was traversed. If`orientation`

is`None`

, the returned relationship has no direction indicated. The direction is respected, but not reported.

#### Output:

`edges: List[Edge]`

➡ A directed relationship indicating the path taken by the depth-first traversal. For graphs, relationship is of the form`(u, v)`

where`u`

and`v`

are the tail and head of the relationship as determined by the traversal. For multigraphs, relationship is of the form`(u, v, key)`

, where`key`

is the key of the relationship. When the graph is directed, then`u`

and`v`

are always in the order of the actual directed relationship. If`orientation`

is not`None`

then the relationship tuple is extended to include the direction of traversal (`forward`

or`reverse`

) on that relationship.

#### Usage:

To get a directed DFS of relationships, run the following query:

```
MATCH (source:Label)
CALL nxalg.edge_dfs(source, 'original')
YIELD edges
RETURN source, edges;
```

`find_cliques()`

Returns all maximal cliques in an undirected graph.

For each node `v`

, a *maximal clique* for `v`

is the largest complete
subgraph containing `v`

. The largest maximal clique is sometimes
called the *maximum clique*.

This function returns an iterator over cliques, each of which is a list of nodes. It is an iterative implementation, so should not suffer from recursion depth issues.

#### Output:

`cliques: List[List[Vertex]]`

➡ An iterator over maximal cliques, each of which is a list of nodes in`G`

. The order of cliques is arbitrary.

#### Usage:

To get all maximal cliques, run the following query:

```
CALL nxalg.find_cliques()
YIELD cliques
RETURN cliques;
```

`find_cycle()`

Returns a cycle found via depth-first traversal.

A *cycle* is a closed path in the graph. The orientation of directed
relationships is determined by `orientation`

.

#### Input:

`source: List[Vertex] (default=NULL)`

➡ The node from which the traversal begins. If`None`

, then a source is chosen arbitrarily and repeatedly until all relationships from each node in the graph are searched.`orientation: string (default=NULL)`

➡ For directed graphs and directed multigraphs, relationship traversals need not respect the original orientation of the relationships. When set to`reverse`

every relationship is traversed in the reverse direction. When set to`ignore`

, every relationship is treated as undirected. When set to`original`

, every relationship is treated as directed. In all three cases, the yielded relationship tuples add a last entry to indicate the direction in which that relationship was traversed. If`orientation`

is`None`

, the yielded relationship has no direction indicated. The direction is respected, but not reported.

#### Output:

`cycle: List[Edge]`

➡ A list of directed relationships indicating the path taken for the loop. If no cycle is found, then an exception is raised. For graphs, an relationship is of the form`(u, v)`

where`u`

and`v`

are the tail and the head of the relationship as determined by the traversal. For multigraphs, an relationship is of the form`(u, v, key)`

, where`key`

is the key of the relationship. When the graph is directed, then`u`

and`v`

are always in the order of the actual directed relationship. If`orientation`

is not`None`

then the relationship tuple is extended to include the direction of traversal (`forward`

or`reverse`

) on that relationship.

#### Usage:

To get a cylce, run the following query:

```
MATCH (n:Node)
WITH collect(n) AS source
CALL nxalg.find_cycle(source)
YIELD cycle
RETURN source, cycle;
```

`flow_hierarchy()`

Returns the flow hierarchy of a directed network.

*Flow hierarchy* is defined as the fraction of relationships not participating
in cycles in a directed graph.

#### Input:

`weight: string (default=NULL)`

➡ Attribute to use for node weights. If`None`

, the weight defaults to 1.

#### Output:

`flow_hierarchy: double`

➡ Flow hierarchy value.

#### Usage:

To get the flow hierarchy of a directed network, run the following query:

```
CALL nxalg.flow_hierarchy()
YIELD
RETURN flow_hierarchy;
```

`global_efficiency()`

Returns the average global efficiency of the graph. The *efficiency* of a pair
of nodes in a graph is the multiplicative inverse of the shortest path distance
between the nodes. The *average global efficiency* of a graph is the average
efficiency of all pairs of nodes.

#### Output:

`global_efficiency: double`

➡ The average global efficiency of the graph.

#### Usage:

To get average global efficiency, run the following query:

```
CALL nxalg.global_efficiency()
YIELD global_efficiency
RETURN global_efficiency;
```

`greedy_color()`

Color a graph using various strategies of greedy graph coloring. Attempts to color a graph using as few colors as possible, where no neighbors of a node can have the same color as the node itself. The given strategy determines the order in which nodes are colored.

#### Input:

`strategy`

➡ The parameter`function(G,colors)`

is a function (or a string representing a function) that provides the coloring strategy, by returning nodes in the order they should be colored.`G`

is the graph, and`colors`

is a dictionary of the currently assigned colors, keyed by nodes. The function must return an iterable over all the nodes in`G`

. If the strategy function is an iterator generator (a function with`yield`

statements), keep in mind that the`colors`

dictionary will be updated after each`yield`

, since this function chooses colors greedily. If`strategy`

is a string, it must be one of the following, each of which represents one of the built-in strategy functions:`'largest_first'`

`'random_sequential'`

`'smallest_last'`

`'independent_set'`

`'connected_sequential_bfs'`

`'connected_sequential_dfs'`

`'connected_sequential'`

(alias for the previous strategy)`'saturation_largest_first'`

`'DSATUR'`

(alias for the previous strategy)

`interchange: boolean (default=False)`

➡ Will use the color interchange algorithm if set to`True`

. Note that`saturation_largest_first`

and`independent_set`

do not work with interchange. Furthermore, if you use interchange with your own strategy function, you cannot rely on the values in the`colors`

argument.

#### Output:

`node: Vertex`

➡ Vertex to color.`color: integer`

➡ Color index of a certain node.

#### Usage:

To color the graph, run the following query:

```
CALL nxalg.greedy_color('connected_sequential_bfs')
YIELD node, color
RETURN node, color;
```

`has_eulerian_path()`

An *Eulerian path* is a path in a graph that uses each relationship of a graph exactly once.

A directed graph has an Eulerian path if:

- at most one vertex has
`out_degree - in_degree = 1`

, - at most one vertex has
`in_degree - out_degree = 1`

, - every other vertex has equal in_degree and out_degree,
- and all of its vertices with nonzero degree belong to a single connected component of the underlying undirected graph.

An undirected graph has an Eulerian path if exactly zero or two vertices have an odd degree and all of its vertices with nonzero degrees belong to a single connected component.

#### Output:

`has_eulerian_path: boolean`

➡`True`

if`G`

has an eulerian path.

#### Usage:

To get Eulerian path, run the following query:

```
CALL nxalg.has_eulerian_path()
YIELD has_eulerian_path
RETURN has_eulerian_path;
```

`has_path()`

Returns `True`

if `G`

has a path from `source`

to `target`

.

#### Input:

`source: Vertex`

➡ Starting node for the path.`target: Vertex`

➡ Ending node for the path.

#### Output:

`has_path: boolean`

➡`True`

if`G`

has a path from`source`

to`target`

.

#### Usage:

To find a path, run the following query:

```
MATCH (n:Label), (m:Label)
CALL nxalg.has_path(n, m)
YIELD has_path
RETURN has_path;
```

`immediate_dominators()`

Returns the immediate dominators of all nodes of a directed graph. The immediate
dominator of a node is the unique node that Strictly dominates a node `n`

but
does not strictly dominate any other node That dominates `n`

.

#### Input:

`start: Vertex`

➡ The start node of dominance computation.

#### Output:

`node: Vertex`

➡ Vertex to calculate dominator for.`dominator: Vertex`

➡ Dominator node for certain vertex.

#### Usage:

To get immediate dominators, run the following query:

```
MATCH (n:Label)
CALL nxalg.immediate_dominators(n)
YIELD node, dominator
RETURN node, dominator;
```

`is_arborescence()`

Returns `True`

if `G`

is an arborescence. An *arborescence* is a directed tree
with maximum in-degree equal to 1.

#### Output:

`is_arborescence: boolean`

➡ A boolean that is`True`

if`G`

is an arborescence.

#### Usage:

To find out if the graph is arborescence, run the following query:

```
CALL nxalg.is_arborescence()
YIELD is_arborescence
RETURN is_arborescence;
```

`is_at_free()`

Check if a graph is AT-free. The method uses the `find_asteroidal_triple`

method
to recognize an AT-free graph. If no asteroidal triple is found, the graph is
AT-free and `True`

is returned. If at least one asteroidal triple is found, the
graph is not AT-free and `False`

is returned.

#### Output:

`is_at_free: boolean`

➡`True`

if`G`

is AT-free and`False`

otherwise.

#### Usage:

To check if the graph is AT-free, run the following query:

```
CALL nxalg.is_at_free()
YIELD is_at_free
RETURN is_at_free;
```

`is_bipartite()`

Returns `True`

if graph `G`

is bipartite, `False`

if not. A *bipartite graph*
(or bigraph) is a graph in which nodes can be divided into two disjoint and
independent sets `u`

and `v`

and such that every relationship connects a vertex
in `u`

one in `v`

.

#### Output:

`is_bipartite: boolean`

➡`True`

if`G`

is bipartite and`False`

otherwise.

#### Usage:

To find out if the graph is bipartite, run the following query:

```
CALL nxalg.is_bipartite()
YIELD is_bipartite
RETURN is_bipartite;
```

`is_branching()`

Returns `True`

if `G`

is a branching. A *branching* is a directed forest with
maximum in-degree equal to 1.

#### Output:

`is_branching: boolean`

➡ A boolean that is`True`

if`G`

is a branching.

#### Usage:

To find out if the graph is branching, run the following query:

```
CALL nxalg.is_branching()
YIELD is_branching
RETURN is_branching;
```

`is_chordal()`

Checks whether `G`

is a chordal graph. A graph is *chordal* if every cycle of
length at least 4 has a chord (an relationship joining two nodes not adjacent in
the cycle).

#### Output:

`is_chordal: boolean`

➡`True`

if`G`

is a chordal graph and`False`

otherwise.

#### Usage:

To check if the graph is chordal, run the following query:

```
CALL nxalg.is_chordal()
YIELD is_chordal
RETURN is_chordal;
```

`is_distance_regular()`

Returns `True`

if the graph is distance regular, `False`

otherwise. A connected
graph `G`

is distance-regular if for any nodes `x,y`

and any integers
`i,j=0,1,...,d`

(where `d`

is the graph diameter), the number of vertices at
distance `i`

from `x`

and distance `j`

from `y`

depends only on `i,j`

and the
graph distance between `x`

and `y`

, independently of the choice of `x`

and `y`

.

#### Output:

`is_distance_regular: boolean`

➡`True`

if the graph is distance regular,`False`

otherwise.

#### Usage:

To check if the graph is distance regular, run the following query:

```
CALL nxalg.is_distance_regular()
YIELD is_distance_regular
RETURN is_distance_regular;
```

`is_edge_cover()`

Decides whether a set of relationships is a valid relationship cover of the
graph. Given a set of relationships, it can be decided whether the set is an
*edge covering* if checked whether all nodes of the graph have an relationship
from the set incident on it.

#### Input:

`cover: List[Edge]`

➡ A list of relationships to be checked.

#### Output:

`is_edge_cover: boolean`

➡ Whether the set of relationships is a valid edge cover of the graph.

#### Usage:

To check if a set of relationshiips is a valid relationship cover of the graph, run the following query:

```
MATCH (n)-[e]-(m)
WITH COLLECT(e) AS cover
CALL nxalg.is_edge_cover(cover)
YIELD is_edge_cover
RETURN is_edge_cover;
```

`is_eulerian()`

Returns `True`

if and only if `G`

is Eulerian. A graph is *Eulerian* if it has
an Eulerian circuit. An *Eulerian circuit* is a closed walk that includes each
relationship of a graph exactly once.

#### Output:

`is_eulerian: boolean`

➡`True`

if`G`

is Eulerian.

#### Usage:

To check if the graph is Eulerian, run the following query:

```
CALL nxalg.is_eulerian()
YIELD is_eulerian
RETURN is_eulerian;
```

`is_forest()`

Returns `True`

if `G`

is a forest. A *forest* is a graph with no undirected
cycles. For directed graphs, `G`

is a forest if the underlying graph is a
forest. The underlying graph is obtained by treating each directed relationship
as a single undirected relationship in a multigraph.

#### Output:

`is_forest: boolean`

➡ A boolean that is`True`

if`G`

is a forest.

#### Usage:

To check if a graph is a forest, run the following query:

```
CALL nxalg.is_forest()
YIELD is_forest
RETURN is_forest;
```

`is_isolate()`

Determines whether a node is an isolate. An *isolate* is a node with no
neighbors (that is, with degree zero). For directed graphs, this means no
in-neighbors and no out-neighbors.

#### Input:

`n: Vertex`

➡ A node in`G`

.

#### Output:

`is_isolate: boolean`

➡`True`

if and only if`n`

has no neighbors.

#### Usage:

To check if a nodes is an isolate, run the following query:

```
MATCH (n)
CALL nxalg.is_isolate(n)
YIELD is_isolate
RETURN is_isolate;
```

`is_isomorphic()`

Returns `True`

if the graphs `G1`

and `G2`

are isomorphic and `False`

otherwise.
The two graphs `G1`

and `G2`

must be the same type.

#### Input:

`nodes1: List[Vertex]`

➡ Nodes in`G1`

.`edges1: List[Edge]`

➡ Edges in`G1`

.`nodes2: List[Vertex]`

➡ Nodes in`G2`

.`edges2: List[Edge]`

➡ Edges in`G2`

.

#### Output:

`is_isomorphic: boolean`

➡`True`

if the graphs`G1`

and`G2`

are isomorphic and`False`

otherwise.

#### Usage:

To check if the graph is isomorphic, run the following query:

```
MATCH (n:Label1)-[e]-(), (r:Label2)-[f]-()
WITH
COLLECT(n) AS nodes1
COLLECT(e) AS edges1
COLLECT(r) AS nodes2
COLLECT(f) AS edges2
CALL nxalg.is_isomorphic(nodes1, edges1, nodes2, edges2)
YIELD is_isomorphic
RETURN is_isomorphic;
```

`is_semieulerian()`

Returns `True`

if `G`

is semi-Eulerian.

`G`

is semi-Eulerian if it has an Eulerian path but no Eulerian circuit.

#### Output:

`is_semieulerian: boolean`

➡`True`

if`G`

is semi-Eulerian.

#### Usage:

To check if the graph is semi-Eulerian, run the following query:

```
CALL nxalg.is_semieulerian()
YIELD is_semieulerian
RETURN is_semieulerian;
```

`is_simple_path()`

Returns `True`

if and only if the given nodes form a simple path in `G`

. A
*simple path* in a graph is a non-empty sequence of nodes in which no node
appears more than once in the sequence and each adjacent pair of nodes in the
sequence is adjacent in the graph.

#### Input:

`nodes: List[Vertex]`

➡ A list of one or more nodes in the graph`G`

.

#### Output:

`is_simple_path: boolean`

➡ Whether the given list of nodes represents a simple path in`G`

.

#### Usage:

To check if the path is simple, run the following query:

```
MATCH (n:Label)
WITH COLLECT(n) AS nodes
CALL nxalg.is_simple_path(nodes)
YIELD is_simple_path
RETURN is_simple_path;
```

`is_strongly_regular()`

Returns `True`

if and only if the given graph is strongly regular.
An undirected graph is *strongly regular* if:

- it is regular,
- each pair of adjacent vertices has the same number of neighbors in common,
- each pair of nonadjacent vertices has the same number of neighbors in common.

Each strongly regular graph is a distance-regular graph. Conversely, if a distance-regular graph has a diameter of two, then it is a strongly regular graph.

#### Output:

`is_strongly_regular: boolean`

➡ Whether`G`

is strongly regular.

#### Usage:

To check if the graph is strongly regular, run the following query:

```
CALL nxalg.is_strongly_regular()
YIELD is_strongly_regular
RETURN is_strongly_regular;
```

`is_tournament()`

Returns `True`

if and only if `G`

is a tournament.

A *tournament* is a directed graph, with neither self-loops nor multi-relationships, in which there is exactly one directed relationship joining each pair of distinct nodes.

#### Output:

`is_tournament: boolean`

➡ Whether the given graph is a tournament graph.

#### Usage:

To check if the graph is a tournament, run the following query:

```
CALL nxalg.is_tournament()
YIELD is_tournament
RETURN is_tournament;
```

`is_tree()`

Returns `True`

if `G`

is a tree. A *tree* is a connected graph with no
undirected cycles. For directed graphs, `G`

is a tree if the underlying graph is
a tree. The underlying graph is obtained by treating each directed relationship
as a single undirected relationship in a multigraph.

#### Output:

`is_tree: boolean`

➡ A boolean that is`True`

if`G`

is a tree.

#### Usage:

To check if the graph is a tree, run the following query:

```
CALL nxalg.is_tree()
YIELD is_tree
RETURN is_tree;
```

`isolates()`

Returns a list of isolates in the graph. An *isolate* is a node with no
neighbors (that is, with degree zero). For directed graphs, this means no
in-neighbors and no out-neighbors.

#### Output:

`isolates: List[Vertex]`

➡ A list of isolates in`G`

.

#### Usage:

To get isolates, run the following query:

```
CALL nxalg.isolates()
YIELD isolates
RETURN isolates;
```

`jaccard_coefficient()`

Compute the Jaccard coefficient of all node pairs in `ebunch`

.

*Jaccard coefficient* compares members of two sets to see which members are shared and which are distinct.

#### Input:

`ebunch: List[List[Vertex]] (default=NULL)`

➡ Jaccard coefficient will be computed for each pair of nodes given in the iterable. The pairs must be given as 2-tuples`(u, v)`

where`u`

and`v`

are nodes in the graph. If`ebunch`

is`None`

then all non-existent relationships in the graph will be used.

#### Output:

`u: Vertex`

➡ First node in pair.`v: Vertex`

➡ Second node in pair.`coef: Vertex`

➡ Jaccard coefficient.

#### Usage:

To calculate the Jaccard coefficient, run the following query:

```
CALL nxalg.jaccard_coefficient()
YIELD u, v, coef
RETURN u, v, coef;
```

`k_clique_communities()`

Find k-clique communities in a graph using the percolation method. A *k-clique
community* is the union of all cliques of size `k`

that can be reached through
adjacent (sharing `k-1`

nodes) k-cliques.

#### Input:

`k: integer`

➡ Size of the smallest clique.`cliques: List[List[Vertex]] (default=NULL)`

➡ Precomputed cliques (use networkx.find_cliques(G)).

#### Output:

`communities: List[List[Vertex]]`

➡ Sets of nodes, one for each k-clique community.

#### Usage:

To find k-clique communities, run the following query:

```
CALL nxalg.k_clique_communities(3)
YIELD communities
RETURN communities;
```

`k_components()`

Returns the approximate k-component structure of a graph `G`

. A *k-component* is
a maximal subgraph of a graph `G`

that has, at least, node connectivity `k`

: we
need to remove at least `k`

nodes to break it into more components. k-components
have an inherent hierarchical structure because they are nested in terms of
connectivity: a connected graph can contain several 2-components, each of which
can contain one or more 3-components, and so forth. This implementation is based
on the fast heuristics to approximate the k-component structure of a graph.
This, in turn, is based on a fast approximation algorithm for finding good lower
bounds of the number of node independent paths between two nodes.

#### Input:

`density: double (default=0.95)`

➡ Density relaxation threshold.

#### Output:

`k: integer`

➡ Connectivity level k`components: List[List[Vertex]]`

➡ List of sets of nodes that form a k-component of level`k`

as values.

#### Usage:

To get the approximate k-component structure of the graph, run the following query:

```
CALL nxalg.k_components(0.8)
YIELD k, components
RETURN k, components;
```

`k_edge_components()`

Returns nodes in each maximal k-edge-connected component in `G`

. A connected
graph is *k-edge-connected* if it remains connected whenever fewer than `k`

relationships are removed. The relationship-connectivity of a graph is the
largest `k`

for which the graph is k-edge-connected.

#### Input:

`k: integer`

➡ Desired relationship connectivity.

#### Output:

`components: List[List[Vertex]]`

➡ A list of k-edge-connected components. Each set of returned nodes will have k-edge-connectivity in the graph`G`

.

#### Usage:

To get k-edge-connected components, run the following query:

```
CALL nxalg.k_edge_components(3)
YIELD components
RETURN components;
```

`local_efficiency()`

Returns the average local efficiency of the graph. The *efficiency* of a pair of
nodes in a graph is the multiplicative inverse of the shortest path distance
between the nodes. The *local efficiency* of a node in the graph is the average
global efficiency of the subgraph induced by the neighbors of the node. The
*average local efficiency* is the average of the local efficiencies of each
node.

#### Output:

`local_efficiency: double`

➡ The average local efficiency of the graph.

#### Usage:

To get the average local efficiency of the graph, run the following query:

```
CALL nxalg.local_efficiency()
YIELD local_efficiency
RETURN local_efficiency;
```

`lowest_common_ancestor()`

Compute the lowest common ancestor of the given pair of nodes.

#### Input:

`node1: Vertex`

➡ A node in the graph.`node2: Vertex`

➡ A node in the graph.

#### Output:

`ancestor: Vertex`

➡ The lowest common ancestor of`node1`

and`node2`

, or default if they have no common ancestors.

#### Usage:

To compute the lowest common ancestor, run the following query:

```
MATCH (n), (m)
WHERE n != m
CALL nxalg.lowest_common_ancestor(n, m)
YIELD ancestor
RETURN n, m, ancestor;
```

`maximal_matching()`

A *matching* is a subset of relationships in which no node occurs more than
once. A *maximal matching* cannot add more relationships and still be a
matching.

#### Output:

`edges: List[Edge]`

➡ A maximal matching of the graph.

#### Usage:

To get maximal matching of the graph, run the following query:

```
CALL nxalg.maximal_matching()
YIELD edges
RETURN edges;
```

`minimum_spanning_tree()`

Returns a minimum spanning tree or forest on an undirected graph `G`

. A *minimum
spanning tree* is a subset of the relationships of a connected, undirected graph
that connects all of the vertices together without any cycles.

#### Input:

`weight: string (default="weight")`

➡ Data key to use for relationship weights.`algorithm: string (default="kruskal")`

➡ The algorithm to use when finding a minimum spanning tree. Valid choices are`kruskal`

,`prim`

, or`boruvka`

.`ignore_nan: boolean (default=False)`

➡ If`NaN`

is found as an relationship weight normally an exception is raised. If`ignore_nan`

is`True`

then that relationship is ignored.

#### Output:

`node: List[Vertex]`

➡ A minimum spanning tree or forest.`edges: List[Edge]`

➡ A minimum spanning tree or forest.

#### Usage:

To get a minimum spanning tree, run the following query:

```
CALL nxalg.minimum_spanning_tree("weight", "prim", TRUE)
YIELD node, edges
RETURN node, edges;
```

`multi_source_dijkstra_path()`

Find shortest weighted paths in G from a given set of source nodes.

Compute shortest path between any of the source nodes and all other reachable nodes for a weighted graph.

#### Input:

`sources: List[Vertex]`

➡ Starting nodes for paths. If this is a set containing a single node, then all paths computed by this function will start from that node. If there are two or more nodes in the set, the computed paths may begin from any one of the start nodes.`cutoff: integer (default=NULL)`

➡ Depth to stop the search. Only return paths with`length <= cutoff`

.`weight: string`

➡ If this is a string, then relationship weights will be accessed via the relationship attribute with this key (that is, the weight of the relationship joining`u`

to`v`

will be`G.edges[u, v][weight]`

). If no such relationship attribute exists, the weight of the relationship is assumed to be one. If this is a function, the weight of an relationship is the value returned by the function. The function must accept exactly three positional arguments: the two endpoints of an relationship and the dictionary of relationship attributes for that relationship. The function must return a number.

#### Output:

`target: Vertex`

➡ Target key for shortest path.`path: List[Vertex]`

➡ Shortest path in a list.

#### Usage:

To find shortest weighted paths, run the following query:

```
MATCH (n:Label)
COLLECT (n) AS sources
CALL nxalg.multi_source_dijkstra_path(sources, 7)
YIELD target, path
RETURN target, path;
```

`multi_source_dijkstra_path_length()`

Find shortest weighted path lengths in `G`

from a given set of source nodes.

Compute the shortest path length between any of the source nodes and all other reachable nodes for a weighted graph.

#### Input:

`sources: List[Vertex]`

➡ Starting nodes for paths. If this is a set containing a single node, then all paths computed by this function will start from that node. If there are two or more nodes in the set, the computed paths may begin from any one of the start nodes.`cutoff: integer (default=NULL)`

➡ Depth to stop the search. Only return paths with`length <= cutoff`

.`weight: string`

➡ If this is a string, then relationship weights will be accessed via the relationship attribute with this key (that is, the weight of the relationship joining`u`

to`v`

will be`G.edges[u, v][weight]`

). If no such relationship attribute exists, the weight of the relationship is assumed to be one. If this is a function, the weight of an relationship is the value returned by the function. The function must accept exactly three positional arguments: the two endpoints of an relationship and the dictionary of relationship attributes for that relationship. The function must return a number.

#### Output:

`target: Vertex`

➡ Target key for shortest path.`length: double`

➡ Shortest path length.

#### Usage:

To find the shortest path length, run the following query:

```
MATCH (n:Label)
COLLECT (n) AS sources
CALL nxalg.multi_source_dijkstra_path_length(sources, 5)
YIELD target, length
RETURN target, length;
```

`node_boundary()`

Returns the node boundary of `nbunch1`

.

The *node boundary* of a set `S`

with respect to a set `T`

is the set of nodes
`v`

in `T`

such that for some `u`

in `S`

, there is an relationship joining `u`

to `v`

. If `T`

is not specified, it is assumed to be the set of all nodes not in
`S`

.

#### Input:

`nbunch1: List[Vertex]`

➡ List of nodes in the graph representing the`S`

set of nodes whose node boundary will be returned.`nbunch2: List[Vertex] (default=NULL)`

➡ List of nodes representing the`T`

target (or “exterior”) set of nodes. If not specified, this is assumed to be the set of all nodes in`G`

not in`nbunch1`

.

#### Output:

`boundary: List[Vertex]`

➡ The node boundary of`nbunch1`

with respect to`nbunch2`

.

#### Usage:

To get node boundary, run the following query:

```
MATCH (n:Label)
COLLECT (n) AS sources1
CALL nxalg.node_boundary(sources1)
YIELD boundary
RETURN boundary;
```

`node_connectivity()`

Returns an approximation for node connectivity for a graph or digraph `G`

.

*Node connectivity* is equal to the minimum number of nodes that must be removed
to disconnect `G`

or render it trivial. By Menger`s theorem, this is equal to the number of node independent paths (paths that share no nodes other than `

source`and`

target`). If `

source`and`

target`nodes are provided, this function returns the local node connectivity: the minimum number of nodes that must be removed to break all paths from source to`

target`in`

G`. This
algorithm is based on a fast approximation that gives a strict lower bound on
the actual number of node independent paths between two nodes. It works for both
directed and undirected graphs.

#### Input:

`source: Vertex (default=NULL)`

➡ Source node.`target: Vertex (default=NULL)`

➡ Target node.

#### Output:

`connectivity: integer`

➡ Node connectivity of`G`

, or local node connectivity if`source`

and`target`

are provided.

#### Usage:

To get an appoximation for node connectivity, run the following query:

```
MATCH (n:Label), (m:Label)
CALL nxalg.node_connectivity(n, m)
YIELD connectivity
RETURN connectivity;
```

`node_expansion(s)`

Returns the node expansion of the set `S`

. The *node expansion* is the quotient
of the size of the node boundary of `S`

and the cardinality of `S`

.

#### Input:

`s: List[Vertex]`

➡ A sequence of nodes in`G`

.

#### Output:

`node_expansion: double`

➡ The node expansion of the set`S`

.

#### Usage:

To get the node expansion, run the following query:

```
MATCH (n:Label)
WITH COLLECT(n) AS s
CALL nxalg.node_expansion(s)
YIELD node_expansion
RETURN node_expansion;
```

`non_randomness()`

Compute the non-randomness of graph `G`

. The first returned value
`non_randomness`

is the sum of non-randomness values of all relationships within
the graph (where the non-randomness of an relationship tends to be small when
the two nodes linked by that relationship are from two different communities).
The second computed value `relative_non_randomness`

is a relative measure that
indicates to what extent graph `G`

is different from random graphs in terms of
probability. When it is close to 0, the graph tends to be more likely generated
by an Erdos Renyi model.

#### Input:

`k: integer (default=NULL)`

➡ The number of communities in`G`

. If`k`

is not set, the function will use a default community detection algorithm to set it.

#### Output:

`non_randomness: double`

➡ Non-randomness of a graph.`relative_non_randomness: double`

➡ Relative non-randomness of a graph.

#### Usage:

To compute the non-randomness of the graph, run the following query:

```
CALL nxalg.non_randomness()
YIELD non_randomness, relative_non_randomness
RETURN non_randomness, relative_non_randomness;
```

`pagerank()`

Returns the PageRank of the nodes in the graph.

PageRank computes a ranking of the nodes in the graph `G`

based on the structure
of the incoming links. It was originally designed as an algorithm to rank web
pages.

#### Input:

`alpha: double (default=0.85)`

➡ Damping parameter for PageRank.`personalization: string (default=NULL)`

➡ The “personalization vector” consisting of a dictionary with a subset of graph nodes as a key and maps personalization value for each subset. At least one personalization value must be non-zero. If not specified, a nodes personalization value will be zero. By default, a uniform distribution is used.`max_iter: integer (default=100)`

➡ Maximum number of iterations in power method eigenvalue solver.`tol: double (default=1e-06)`

➡ Error tolerance used to check convergence in power method solver.`nstart: string (default=NULL)`

➡ Starting value of PageRank iteration for each node.`weight: string (default="weight")`

➡ Relationship data key to use as weight. If`None`

, weights are set to 1.`dangling: string (default=NULL)`

➡ The outedges to be assigned to any “dangling” nodes, i.e., nodes without any outedges. The dict key is the node the outedge points to and the dict value is the weight of that outedge. By default, dangling nodes are given outedges according to the personalization vector (uniform if not specified). This must be selected to result in an irreducible transition matrix. It may be common to have the dangling dict to be the same as the personalization dict.

#### Output:

`node: Vertex`

➡ Node to calculate PageRank for.`rank: double`

➡ Node PageRank.

#### Usage:

To calculate PageRank, run the following query:

```
CALL nxalg.pagerank()
YIELD node, rank
RETURN node, rank;
```

`reciprocity()`

Compute the reciprocity in a directed graph. The *reciprocity* of a directed
graph is defined as the ratio of the number of relationships pointing in both
directions to the total number of relationships in the graph. The reciprocity of
a single node `u`

is defined similarly, it is the ratio of the number of
relationships in both directions to the total number of relationships attached
to node `u`

.

#### Input:

`nodes: List[Vertex]`

➡ Compute reciprocity for nodes in this container.

#### Output:

`node: Vertex`

➡ Node to calculate reciprocity.`reciprocity: double`

➡ Reciprocity value.

#### Usage:

To compute the reciprocity, run the following query:

```
MATCH(n:Label)
WITH COLLECT(n) AS nodes
CALL nxalg.reciprocity(nodes)
YIELD node, reciprocity
RETURN node, reciprocity;
```

`shortest_path()`

Compute shortest paths in the graph.

#### Input:

`source: Vertex (default=NULL)`

➡ Starting node for the path. If not specified, compute shortest path lengths using all nodes as source nodes.`target: Vertex (default=NULL)`

➡ Ending node for the path. If not specified, compute shortest path lengths using all nodes as target nodes.`weight: string (default=NULL)`

➡ If`None`

, every relationship has weight/distance/cost 1. If a string, use this relationship attribute as the relationship weight. Any relationship attribute not present defaults to 1.`method: string (default="dijkstra")`

➡ The algorithm to use to compute the path length. Supported options:`dijkstra`

,`bellman-ford`

. Other inputs produce a ValueError. If`weight`

is`None`

, unweighted graph methods are used and this suggestion is ignored.

#### Output:

`source: Vertex`

➡ Source node.`target: Vertex`

➡ Target node.`path: List[Vertex]`

➡ All returned paths include both the`source`

and`target`

in the path. If the`source`

and`target`

are both specified, return a single list of nodes in a shortest path from the`source`

to the`target`

. If only the`source`

is specified, return a dictionary keyed by targets with a list of nodes in a shortest path from the`source`

to one of the targets. If only the`target`

is specified, return a dictionary keyed by sources with a list of nodes in a shortest path from one of the sources to the`target`

. If neither the`source`

nor`target`

are specified return a dictionary of dictionaries with`path[source][target]=[list of nodes in path]`

.

#### Usage:

To compute shortest paths, run the following query:

```
MATCH (n:Label), (m:Label)
CALL nxalg.shortest_path(n, m)
YIELD source, target, path
RETURN source, target, path;
```

`shortest_path_length()`

Compute shortest path lengths in the graph.

#### Input:

`source: Vertex (default=NULL)`

➡ Starting node for the path. If not specified, compute shortest path lengths using all nodes as source nodes.`target: Vertex (default=NULL)`

➡ Ending node for the path. If not specified, compute shortest path lengths using all nodes as target nodes.`weight: string (default=NULL)`

➡ If`None`

, every relationship has weight/distance/cost 1. If a string, use this relationship attribute as the relationship weight. Any relationship attribute not present defaults to 1.`method: string (default="dijkstra")`

➡ The algorithm to use to compute the path length. Supported options:`dijkstra`

,`bellman-ford`

. Other inputs produce a ValueError. If`weight`

is`None`

, unweighted graph methods are used and this suggestion is ignored.

#### Output:

`source: Vertex`

➡ Source node.`target: Vertex`

➡ Target node.`length: double`

➡ If the`source`

and`target`

are both specified, return the length of the shortest path from the`source`

to the`target`

. If only the`source`

is specified, return a dict keyed by`target`

to the shortest path length from the`source`

to that`target`

. If only the`target`

is specified, return a dict keyed by`source`

to the shortest path length from that`source`

to the`target`

. If neither the`source`

nor`target`

are specified, return an iterator over (source, dictionary) where dictionary is keyed by`target`

to shortest path length from`source`

to that`target`

.

#### Usage:

To compute shortest path lenghts, run the following query:

```
MATCH (n:Label), (m:Label)
CALL nxalg.shortest_path_length(n, m)
YIELD source, target, length
RETURN source, target, length;
```

`simple_cycles()`

Find simple cycles (elementary circuits) of a directed graph. A *simple cycle*,
or *elementary circuit*, is a closed path where no node appears twice. Two
elementary circuits are distinct if they are not cyclic permutations of each
other. This is a nonrecursive, iterator/generator version of Johnson’s
algorithm. There may be better algorithms for some cases.

#### Output:

`cycles: List[List[Vertex]]`

➡ A list of elementary cycles in the graph. Each cycle is represented by a list of nodes in the cycle.

#### Usage:

TO find simple cycles, run the following query:

```
CALL nxalg.simple_cycles()
YIELD cycles
RETURN cycles;
```

`strongly_connected_components()`

Returns nodes in strongly connected components of a graph.

#### Output:

`components: List[List[Vertex]]`

➡ A list of lists of nodes, one for each strongly connected component of`G`

.

#### Usage:

To get nodes in a stronly connected components, run the following query:

```
CALL nxalg.strongly_connected_components()
YIELD components
RETURN components;
```

`topological_sort()`

Returns nodes in a topologically sorted order. A *topological sort* is a non
unique permutation of the nodes such that an relationship from `u`

to `v`

implies that `u`

appears before `v`

in the topological sort order.

#### Output:

`nodes: List[Vertex]`

➡ A list of nodes in topological sorted order.

#### Usage:

To return nodes in a topologically sorted order, run the following query:

```
CALL nxalg.topological_sort()
YIELD nodes
RETURN nodes;
```

`triadic_census()`

Determines the triadic census of a directed graph. The *triadic census* is a
count of how many of the 16 possible types of triads are present in a directed
graph.

#### Output:

`triad: string`

➡ Triad name.`count: integer`

➡ Number of occurrences as value.

#### Usage:

To determine the triadic census, run the following query:

```
CALL nxalg.triadic_census()
YIELD triad, count
RETURN triad, count;
```

`voronoi_cells()`

Returns the Voronoi cells centered at center_nodes with respect to the
shortest-path distance metric. If `C`

is a set of nodes in the graph and `c`

is
an element of `C`

, the *Voronoi cell* centered at a node `c`

is the set of all
nodes `v`

that are closer to `c`

than to any other center node in `C`

with
respect to the shortest-path distance metric. For directed graphs, this will
compute the “outward” Voronoi cells in which distance is measured from the
center nodes to the target node.

#### Input:

`center_nodes: List[Vertex]`

➡ A nonempty set of nodes in the graph`G`

that represent the centers of the Voronoi cells.`weight: string (default=NULL)`

➡ The relationship attribute (or an arbitrary function) representing the weight of an relationship. This keyword argument is as described in the documentation for`networkx.multi_source_dijkstra_path`

, for example.

#### Output:

`center: Vertex`

➡ Vertex value of center_nodes.`cell: List[Vertex]`

➡ Partition of`G`

closer to that center node.

#### Usage:

To get the Vornoi cells, run the following query:

```
MATCH (n)
WITH COLLECT(n) AS center_nodes
CALL nxalg.voronoi_cells(center_nodes)
YIELD counter, cell
RETURN center, cell;
```

`wiener_index()`

Returns the Wiener index of the given graph. The *Wiener index* of a graph is
the sum of the shortest-path distances between each pair of reachable nodes. For
pairs of nodes in undirected graphs, only one orientation of the pair is
counted.

#### Input:

`weight: string (default=NULL)`

➡ The relationship attribute to use as distance when computing shortest-path distances. This is passed directly to the`networkx.shortest_path_length`

function.

#### Output:

`wiener_index: double`

➡ The Wiener index of the graph`G`

.

#### Usage:

To get the Wiener index, run the following query:

```
CALL nxalg.voronoi_cells()
YIELD weiner_index
RETURN wiener_index;
```