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Graphs & Unsupervised Machine Learning



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




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