Introduction

Ultipa offers an ever-growing rich set of algorithms for graph analytics purposes, many of which can achieve real-time computing, and some algorithms running on the full graph can achieve near-real-time effect through asynchronous tasks. The Ultipa algorithm package is provided to users in the form of hot-pluggable plugins, and the installation, update, or removal of any algorithm will not affect the operation of the server. Users can purchase standard algorithm packages or custom algorithm packages.

This manual contains the following content:

• Using Algorithms
  • Show Algorithms
  • Install and Uninstall
  • Run Algorithms
  • Algorithm Results and Statistics
• Centrality Algorithms
  • Degree Centrality
  • Closeness Centrality
  • Harmonic Centrality
  • Graph Centrality
  • Betweenness Centrality
  • Eigenvector Centrality
  • CELF
  • PageRank
  • ArticleRank
  • HITS
  • SybilRank
• Similarity algorithms
  • Jaccard Similarity
  • Overlap Similarity
  • Cosine Similarity
  • Pearson Correlation Coefficient
  • Euclidean Distance
• Connectivity & Compactness Algorithms
  • K-Hop All
  • Bipartite Graph
  • HyperANF
  • Connected Component
  • Triangle Counting
  • Induced Subgraph
  • k-Core
  • k-Truss
  • p-Cohesion
  • k-Edge Connected Components
  • Local Clustering Coefficient
  • Topological Sort
  • Schema Overview
• Pathfinding
  • Dijkstra's Single-Source Shortest Path
  • Delta-Stepping Single-Source Shortest Path
  • Shortest Path Faster Algorithm (SPFA)
  • Minimum Spanning Tree
  • Breadth-First Seach (BFS)
  • Depth First Search (DFS)
• Topological Link Prediction
  • AA Index
  • Common Neighbors
  • Preferential Attachment
  • Resource Allocation
  • Total Neighbors
• Community Detection & Classification Algorithms
  • Louvain
  • Leiden
  • Label Propagation
  • HANP
  • k-Means
  • kNN (k-Nearest Neighbors)
• Graph Embedding Algorithms
  • Algorithms
    • Random Walk
    • Node2Vec Walk
    • Node2Vec
    • Struc2Vec Walk
    • Struc2Vec
    • GraphSAGE
    • GraphSAGE Train
    • LINE
    • Fast Random Projection
  • Background Knowledge
    • Summary of Graph Embedding
    • Gradient Descent
    • Backpropagation
    • Skip-gram
    • Skip-gram Optimization

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