Playground goes beyond case studies: learn which graph algos to use with your streams, master graph algorithms in minutes and get a jump start on your graph stream project.
Find /r/worldnews subreddit's most prominent redditors. The dataset is constantly updated with new data. The dataset lives in a Memgraph instance that is connected to a Kafka stream which brings live /r/worldnews data to our dataset.
The property graph model is perfect for representing networks like Reddit. Try out a simple query like MATCH (n)-[r]-(m) RETURN n, m, r and see for yourself.
Traversing your data in graph format is much simpler than dealing with relational models. For example, try to find the redditor with the highest number of posted submissions and comments.
The MAGE library contains many graph algorithms including those offered by the NetworkX Python package. Take a look at how we used the NetworkX method bfs_tree() to search for influential redditor.
You can also implement custom procedures in Python, Rust, and C/C++. We created a small sentiment analysis module that calculates the sentiment of submissions and comments.
The PageRank algorithm is a method of measuring the importance of nodes by analyzing the quantity and quality of the links that point to them. Google uses this algorithm to rank search results but it can also be used for developing recommendation systems, influence analysis, and much more.
Groups of densely connected nodes are easy to spot visually, but more sophisticated methods are needed to perform these tasks programmatically. Community detection algorithms are used to find such groups of densely connected components in various networks.
A very common use case for community detection algorithms is social network analysis where the algorithms are used to identify groups of users based on interactions.
Breadth-First Search (BFS) is a way of traversing graph data structures. The traversal starts from a single node and, during the traversal, breadth is prioritized over depth, hence the name of the algorithm. This algorithm can be used to solve many problems including pathfinding and cycle detection.
Betweenness centrality measures the extent to which a vertex or edge lies on paths between vertices. Vertices and edges with high betweenness may have considerable influence within a network by virtue of their control over information passing between others.
