Ultipa offers an ever-growing rich set of algorithms for graph analytics purposes, including various algorithms of degree, centrality, community/clustering (such as Louvain, LPA), graph embedding (such as Random Walk, Node2Vec, Line), and graph traversal related (such as K-Hop Whole Graph, etc.).
Many Ultipa algorithms can compute in a real-time fashion, such as the similarity between two nodes; some whole-graph and whole-data running algorithms can achieve near-real-time effect through asynchronous tasks, such as PageRank, LPA, etc.
Ultipa algorithm package is offered to users as a hot-pluggable plugin that can be hot-updated. Both advanced algorithm package and custom algorithm package are available to users.
Category | Examples |
---|---|
Basic algorithm | Degree, Centrality, Similarity |
General algorithm | K-Hop Whole Graph, Connected Component, Triangle Counting, Common Neighbors, Induced Subgraph, Bipartite Graph |
Advanced algorithm | HyperANF, K-NearestNeighbor, k-Core, MST, k-Means, Local Clustering Coefficient |
Label propagation algorithm | PageRank, Sybil Rank, Label Propagation, HANP |
Community detection algorithm | Louvain Community Recognition |
Graph embedding algorithm | Random Walk, Node2Vec, Struc2Vec, LINE, Fast RP, GraphSage |
Algorithm that calculates nodes | Degree, Centrality, Similarity, Common Neighbors |
Algorithm that performs calculations on the whole graph | Algorithms that are not single node oriented |
Algorithm that can be completed in real time for 10 million nodes/edges and below | Louvain Community Recognition |
AI algorithm | Random Walk, Node2Vec, Struc2Vec, LINE, Fast RP, GraphSage |