I tried modeling network topology as a graph using Neo4j. To show its effectiveness, I used large scale typologies ranging from 10875, 26518, 36682 to 62586 nodes.
I run the shortest path algorithm to find the shortest path between two nodes (source and target). I want to benefit from other algorithms in the GDS library. any suggestions?
What about centrality algorithms like Betweenness, Closeness, etc. can it be useful if applied in the domain of networking?
Hi, @samasamaan !
I would suggest Betweenness Centrality for identifying vulnerable and bridge nodes, as well as uncovering key transfer points in network. Also, you could use K1 Coloring for scheduling optimization. Pathfinding algorithms are fully recommended for identifying optimal routes.
Until now Betweenness Centrality does not support weighted graphs.
In your opinion, is it still effective even without considering weights?
Of course! It is one of the most important centrality algorithms. The algorithms works as follows:
The algorithm calculates unweighted shortest paths between all pairs of nodes in a graph. Each node receives a score, based on the number of shortest paths that pass through the node. Nodes that more frequently lie on shortest paths between other nodes will have higher betweenness centrality scores.
You can check it out on the documentation: Betweenness Centrality - Neo4j Graph Data Science
What about K-1 coloring algorithm? I run it and no colors are assigned to nodes. Only numbers. Also I didn't understand what you meant by scheduling optimization.
I read about it in the Neo4j Doc & it is still in beta phase.