Hot Algo Summer: New Algorithms in Neo4j Graph Data Science

Neo4j Graph Data Science has introduced three new algorithms: K-Core Decomposition, Bellman-Ford shortest path algorithm (SPFA), and Common Neighbour Aware Random Walk (CNARW). These innovative algorithms can solve pain points such as negative weights in pathfinding, community detection with varying density levels, and efficient subgraph sampling. The K-Core Decomposition algorithm is used for community detection, allowing users to analyze community structures and identify influential nodes. The Bellman-Ford shortest path algorithm enables the calculation of the shortest paths in graphs with negative weights, which can be useful in IT networking, supply chain optimization, and finance. The Common Neighbour Aware Random Walk (CNARW) algorithm is a graph sampling technique that helps scale machine learning on large graphs by considering common neighbors of nodes. These new offerings provide Data Scientists with expanded tools to refine the ability to find the right algorithm for the right problem.