UltipaDocs
Try Playground
  • Introduction
  • Managing HDC Graphs
  • Managing Distributed Projections
  • Installing Algorithms
  • Running Algorithms
    • Degree Centrality
    • Closeness Centrality
    • Harmonic Centrality
    • Graph Centrality
    • Betweenness Centrality
    • Eigenvector Centrality
    • Katz Centrality
    • CELF
    • PageRank
    • ArticleRank
    • TextRank
    • HITS
    • SybilRank
    • Jaccard Similarity
    • Overlap Similarity
    • Cosine Similarity
    • Pearson Correlation Coefficient
    • Euclidean Distance
    • 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
    • Dijkstra's Single-Source Shortest Path
    • Delta-Stepping Single-Source Shortest Path
    • Shortest Path Faster Algorithm (SPFA)
    • Minimum Spanning Tree
    • Breadth-First Search (BFS)
    • Depth-First Search (DFS)
    • Adamic-Adar Index
    • Common Neighbors
    • Preferential Attachment
    • Resource Allocation
    • Total Neighbors
    • Louvain
    • Leiden
    • Label Propagation
    • HANP
    • k-Means
    • kNN (k-Nearest Neighbors)
    • K-1 Coloring
    • Conductance
      • Random Walk
      • Node2Vec Walk
      • Node2Vec
      • Struc2Vec Walk
      • Struc2Vec
      • LINE
      • Fast Random Projection
      • Summary of Graph Embedding
      • Gradient Descent
      • Backpropagation
      • Skip-gram
      • Skip-gram Optimization
  1. Docs
  2. /
  3. Graph Analytics & Algorithms

Introduction

Ultipa offers a rich set of graph algorithms designed for gaining analytical insights from graph data. These algorithms can be executed using both GQL and UQL.

How to Run Algorithms

To achieve optimal performance, it is essential to create either HDC graphs or distributed projections for the graphsets. Algorithms should be run on an HDC graph or a distributed projection, rather than directly on the original graphset.

For more information, see:

  • Managing HDC Graphs
  • Managing Distributed Projections
  • Running Algorithms

All algorithms can be run on HDC graphs, and some support distributed projections. Each algorithm page displays the following tags:

HDC - Indicates the algorithm supports an HDC version.

Distributed - Indicates the algorithm supports a distributed version.

How to Install Algorithms

Algorithms for HDC and distributed versions are managed differently. For more details, see Installing Algorithms.

All Algorithms

The algorithms Ultipa provides are classified into the following categories:

  • Centrality Algorithms
    • Degree Centrality
    • Closeness Centrality
    • Harmonic Centrality
    • Graph Centrality
    • Betweenness Centrality
    • Eigenvector Centrality
    • Katz Centrality
    • CELF
    • PageRank
    • ArticleRank
    • TextRank
    • 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 Search (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)
    • K-1 Coloring
    • Conductance
  • Graph Embedding Algorithms
    • Algorithms
      • Random Walk
      • Node2Vec Walk
      • Node2Vec
      • Struc2Vec Walk
      • Struc2Vec
      • LINE
      • Fast Random Projection
    • Background Knowledge
      • Summary of Graph Embedding
      • Gradient Descent
      • Backpropagation
      • Skip-gram
      • Skip-gram Optimization