Ultipa offers an ever-growing rich set of algorithms for graph analytics purposes, many of them can be executed in a real-time fashion, some whole-graph and whole-data running algorithms can achieve near-real-time effect through asynchronous tasks. 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.

This manual contains the following content:

• Using Algorithms
  • Install and Uninstall
  • Show Algorithms
  • Command and Parameters
  • Execution Result
  • Execution Method
• Centrality Algorithms
  • Degree
  • Closeness Centrality
  • Harmonic Centrality
  • Graph Centrality
  • Betweenness Centrality
  • Eigenvector Centrality
  • CELF
  • PageRank & ArticleRank
  • SybilRank
• Similarity algorithms
  • Jaccard Similaity
  • Overlap Similarity
  • Cosine Similarity
  • Pearson Correlation Coefficient
  • Euclidean Distance
• Connectivity & Compactness Algorithms
  • K-Hop All
  • Connected Component
  • Triangle Counting
  • Induced Subgraph
  • Bipartite Graph
  • HyperANF
  • k-Core
  • Minimum Spanning Tree
  • Local Clustering Coefficient
• Topological Link Prediction
  • AA Index
  • Common Neighbors
  • Preferential Attachment
  • Resource Allocation
  • Total Neighbors
• Propagation Algorithms
  • Label Propagation
  • HANP
• Community Detection & Classification Algorithms
  • k-Nearest Neighbor
  • k-Means
  • Louvain
• Graph Embedding Algorithms
  • Backgroud Knowledge
    • What is Graph Embedding
    • Gradient Descent
    • Backpropagation
    • Skip-gram Model
    • Skip-gram Model Optimization
  • Algorithms
    • Random Walk
    • Node2Vec Walk
    • Node2Vec
    • Struc2Vec Walk
    • Struc2Vec
    • GraphSAGE
    • GraphSAGE Train
    • LINE
    • Fast Random Projection

If you have any questions about the manual, please do not hesitate to contact our team. Documentation license: Creative Commons 4.0! © 2021 Ultipa, Inc.