Unsupervised Machine Learning in Graphs.
Types of graphs:
Apache Jena Fuseki
AWS Neptune
Neo4j
Following are algorithms in unsupervised ML for the following tasks:
Centrality & Importance
PageRank
Degree Centrality
Harmonic Centrality
Pathfinding & Search
Shortest Path
A* Shortest Path
Minimum Weight Spanning Tree
Random Walk
Breadth & Depth First Search
K-Spanning Tree
Similarity
Node Similarity
K-Nearest Neighbors KNN
Cosine Similarity (Word2Vec)
Graph Embeddings
Node2Vec
FastRP
GraphSAGE
Heuristic Link
Adamic Adar
Common Neighbors
Total Neighbors
Community Detection
Triangle Count
Local Clustering Coefficient