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v4.0
    v4.0

    Graph Analytics and Algorithms

    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
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