# igraphalg

## Abstract

The **igraphalg** module provides a comprehensive set of thin wrappers around some of the algorithms present in the igraph package. The wrapper functions can create an igraph compatible graph-like object that can stream the native database graph directly, significantly lowering memory usage.

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

Module type | module |

Implementation | Python |

Graph direction | directed/undirected |

Edge weights | weighted/unweighted |

Parallelism | sequential |

## Procedures

`get_all_simple_paths(v, to, cutoff)`

Returns all simple paths in the graph `G`

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

#### Input:

`v: Vertex`

➡ Path's starting node.`to: Vertex`

➡ Path's ending node.`cutoff: int (default=-1)`

➡ Maximum length of the considered path. If negative, paths of all lengths are considered.

#### Output:

`path: List[Vertex]`

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

#### Usage:

`MATCH (n:Label), (m:Label)`

CALL igraphalg.get_all_simple_paths(n, m, 5) YIELD *

RETURN path;

`spanning_tree(weights, directed)`

Returns a minimum spanning tree on a graph `G`

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

#### Input:

`weights: string (default=NULL)`

➡ Data key to use for edge weights.`directed: bool (default=False)`

➡ If`true`

the graph is directed, otherwise it's undirected.

#### Output:

`tree: List[List[Vertex]]`

➡ A minimum spanning tree or forest.

#### Usage:

`CALL igraphalg.spanning_tree() `

YIELD *

RETURN tree;

`pagerank(damping, weights, directed,implementation)`

Returns the PageRank of the nodes in the graph.

PageRank computes a ranking of the nodes in graph G based on the structure of the incoming links. It was originally designed as an algorithm to rank web pages.

#### Input:

`damping: double (default=0.85)`

➡ Damping parameter for PageRank.`weights: string (default="weight")`

➡ Edge data key to use as a weight. If`None`

, weights are set to 1.`directed: bool (default=True)`

➡ If`true`

the graph is directed, otherwise it's undirected.`implementation: string (default="prpack")`

➡ Algorithm used for calculating PageRank values. The algorithm can be either`prpack`

or`arpack`

.

#### Output:

`node: Vertex`

➡ Vertex for which the PageRank is calculated.`rank: double`

➡ Node's PageRank value.

#### Usage:

`CALL igraphalg.pagerank() YIELD *`

RETURN node, rank;

`get_shortest_path(source, target, weights, directed)`

Compute the shortest path in the graph.

#### Input:

`source: Vertex (default=NULL)`

➡ Path's starting node.`target: Vertex (default=NULL)`

➡ Path's ending node.`weights: string (default=NULL)`

➡ If`None`

, every edge has weight/distance/cost 1. If the value is a property name, use that property as the edge weight. If an edge doesn't have a property, the value defaults to 1.`directed: bool (default=True)`

➡ If`true`

, the graph is directed, otherwise, it's undirected.

#### Output:

`path: List[Vertex]`

➡ Path between`source`

node and`target`

node.

#### Usage:

`MATCH (n:Label), (m:Label)`

CALL igraphalg.get_shortest_path(n, m) YIELD *

RETURN path;

`shortest_path_length(source, target, weights, directed)`

Compute the shortest path length in the graph.

#### Input:

`source: Vertex (default=NULL)`

➡ Path's starting node.`target: Vertex (default=NULL)`

➡ Path's ending node.`weights: string (default=NULL)`

➡ If`None`

, every edge has weight/distance/cost 1. If the value is a property name, use that property as the edge weight. If an edge doesn't have a property, the value defaults to 1.`directed: bool (default=True)`

➡ If`true`

, the graph is directed, otherwise, it's undirected.

#### Output:

`length: double`

➡ Shortest path length between the`source`

node and`target`

node. If there is no path it returns`inf`

.

#### Usage:

`MATCH (n:Label), (m:Label)`

CALL igraphalg.shortest_path_length(n, m) YIELD length

RETURN length;

`topological_sort(mode)`

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

to `v`

implies that `u`

appears before `v`

in the topological sort order.

#### Input:

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

➡ Specifies how to use the direction of the edges. For`out`

, the sorting order ensures that each node comes before all nodes to which it has edges, so nodes with no incoming edges go first. For`in`

, it is quite the opposite: each node comes before all nodes from which it receives edges. Nodes with no outgoing edges go first.

#### Output:

`nodes: List[Vertex]`

➡ A list of nodes in topological sorted order.

#### Usage:

`CALL igraphalg.topological_sort() YIELD *`

RETURN nodes;

`maxflow(source, target, capacity)`

The maximum flow problem consists of finding a flow through a graph such that it is the maximum possible flow.

#### Input:

`source: Vertex`

➡ Source node from which the maximum flow is calculated.`target: Vertex`

➡ Sink node to which the max flow is calculated.`capacity: string (default="weight")`

➡ Edge property which is used as the flow capacity of the edge.

#### Output:

`max_flow: Number`

➡ Maximum flow of the network, from source to sink

#### Usage:

`MATCH (source {id: 0}), (sink {id: 5})`

CALL igraphalg.maxflow(source, sink, "weight")

YIELD max_flow

RETURN max_flow;

`mincut(source, target, capacity,directed)`

Minimum cut calculates the minimum st-cut between two vertices in a graph.

#### Input:

`source: Vertex`

➡ Source node from which the maximum flow is calculated.`target: Vertex`

➡ Sink node to which the max flow is calculated.`capacity: string (default="weight")`

➡ Edge property which is used as the capacity of the edge.

#### Output:

`node: Vertex`

➡ Vertex in graph.`partition_id: int`

➡ Id of the partition where`node`

belongs after min-cut.

#### Usage:

` MATCH (source {id: 0}), (sink {id: 5})`

CALL igraphalg.mincut(source, sink)

YIELD node, partition_id

RETURN node, partition_id;

`community_leiden(objective_function, weights, resolution_parameter, beta, initial_membership, n_iterations, node_weights)`

Finding community structure of a graph using the Leiden algorithm of Traag, van Eck & Waltman.

#### Input:

`objective_function: string (default="CPM")`

➡ Whether to use the Constant Potts Model (CPM) or modularity. Must be either`CPM`

or`modularity`

.`weights: string (default=NULL)`

➡ If a string is present, use this edge attribute as the edge weight if it isn't edge weights default to 1.`resolution_parameter: float (default=1.0)`

➡ Higher resolutions lead to smaller communities, while lower resolutions lead to fewer larger communities.`beta: float (default=0.01)`

➡ Parameter affecting the randomness in the Leiden algorithm. This affects only the refinement step of the algorithm.`initial_membership: List[int](default=NULL)`

➡ If provided, the Leiden algorithm will try to improve this provided membership. If no argument is provided, the algorithm simply starts from the singleton partition.`n_iterations: int (default=2)`

➡ The number of iterations to iterate the Leiden algorithm. Each iteration may improve the partition further.`vertex_weights: List[float] (default=NULL)`

➡ The vertex weights used in the Leiden algorithm. If this is not provided, it will be automatically determined based on the objective_function.

#### Output:

`node: Vertex`

➡ Vertex in graph.`community_id: int`

➡ Id of community where`node`

belongs.

#### Usage:

` CALL igraphalg.community_leiden() `

YIELD node, community_id

RETURN node, community_id;

`all_shortest_path_lengths( weights, directed)`

Compute all shortest path lengths in the graph.

#### Input:

`weights: string (default=NULL)`

➡ If`None`

, every edge has weight/distance/cost 1. If the value is a property name, use that property as the edge weight. If an edge doesn't have a property, the value defaults to 1.`directed: bool (default=True)`

➡ If`true`

, the graph is directed, otherwise, it's undirected.

#### Output:

`src_node: Vertex`

➡`Source`

node.`dest_node: Vertex`

➡`Destination`

node.`length: double`

➡ If`true`

, the graph is directed, otherwise, it's undirected.

#### Usage:

`CALL igraphalg.all_shortest_path_length()`

YIELD src_node, dest_node, length

RETURN src_node, dest_node, length;