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.
|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|