Join GitHub today
GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.Sign up
GSQL Graph Algorithms
|Type||Name||Latest commit message||Commit time|
|Failed to load latest commit information.|
README for GSQL Algorithm Library 9/12/19 The GSQL Graph Algorithm Library is a collection of high-performance GSQL queries, each of which implements a standard graph algorithm. Each algorithm is ready to be installed and used, either as a stand-alone query or as a building block of a larger analytics application.GSQL running on the TigerGraph platform is particularly well-suited for graph algorithms: * Turing-complete with full support for imperative and procedural programming, ideal for algorithmic computation. * Parallel and Distributed Processing, enabling computations on larger graphs. * User-Extensible. Because the algorithms are written in standard GSQL and compiled by the user, they are easy to modify and customize. * Open-Source. Users can study the GSQL implementations to learn by example, and they can develop and submit additions to the library. Compatibility ----------------- v1.2 compatible with TG2.4 or higher v1.1 compatible with TG2.4 or higher Library Structure ----------------- You can download the library from github: https://github.com/tigergraph/gsql-graph-algorithms The library contains two main sections: Algorithms and Tests. The Algorithms folder contains template algorithms and scripts to help you customize and install them. There are two folders: templates/ contains template algorithms with some placeholder code and markers which need to be acted on by the installation script. examples/ contains GSQL queries generated from the templates by the installation script. The Tests folder contains small sample graphs that you can use to experiment with the algorithms. In our online documentation, we use the test graphs to show you the expected result for each algorithm. The graphs are small enough that you can manually calculate and sometimes intuitively see what the answers should be. Get Started ----------- If you want to use one of the test graphs, load it before installing the algorithms: See the README.test file in the tests folder * Install Algorithms: 1) You should create a graph schema in GSQL first. 2) Change into the algorithms folder. 3) Run a installation script, i.e., bash install.sh and answer the questions. More detailed documentation and examples are available on the web at https://docs.tigergraph.com/graph-algorithm-library List of GSQL Graph Algorithms ----------------------------- as of Sept 5, 2019 Compatible with TigerGraph version 2.1.8 or higher closeness_cent Closeness Centrality conn_comp Connected Component Detection scc Strongly Connected Component Detection label_prop Label Propagation Method for Community Detection louvain_parallel Parallel Louvain Modularity Method with Refinement for Community Detection pageRank PageRank measurement of relative influence of each vertex pageRank_wt Weighted PageRank pageRank_pers Personalized PageRank shortest_ss_no_wt Single-Source Shortest Paths without weight shortest_ss_pos_wt Single-Source Shortest Paths with positive weight shortest_ss_any_wt Single-Source Shortest Paths mst Minimum Spanning Tree (MST) cycle_detection Rocha–Thatte algorithm for cycle detection tri_count Count all the triangles, memory effient tri_count_fast Count all the triangles, faster but using more memory cosine_nbor_ss Cosine Similarity from a single vertex cosine_nbor_ap Cosine Similarity for each pair of vertices jaccard_nbor_ss Jaccard Similarity from a single vertex jaccard_nbor_ap Jaccard Similarity for each pair of vertices knn_cosine_ss k-Nearest Neighbor classification, using Cosine Similarity, single source knn_cosine_all k-Nearest Neighbor classification, using Cosine Similarity, batch knn_cosine_cv Cross validation for k-Nearest Neighbor, using Cosine Similarity Each of the above may be available as 2 or 3 related queries For example: pageRank.gsql - base version. Results are provided as JSON output. Not persisted to the graph database. pageRank_file.gsql - Results are in CSV format to a file. Not persisted to the graph database. pageRank_attr.gsql - Results are written to vertex or edge attributes which the user specifies.