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TigerGraph In-Database Graph Data Science Algorithm Library

For TigerGraph version 3.1 or higher 10/5/2021

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.

Library Structure

You can download the library from github: https://github.com/tigergraph/gsql-graph-algorithms

The library contains e main sections: algorithms and graphs, and UDF.

The algorithms folder has two levels of subfolders to categorize by category and algorithm. Within each algorithm folder is 1 or more algorithm queries, a README and a CHANGELOG file. If any subqueries, UDF (user-defined function), or other auxiliary files are needed, they are also there.

The graphs 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.

The UDF folder contains an ExprFunctions.hpp file which contains custom C++ functons which extend the GSQL query language and are needed by certain algorithms.

NOTES:

  1. Currently, each TigerGraph installation has one global ExprFunctions.hpp which must contain the UDFs for all queries. Therefore, the ExprFunctions.hpp file(s) you find here need to be merged with the global ExprFunctions.hpp. Section docs.tigergraph.com for more information about Installing User-Defined Functions with the GET and PUT commands.
  2. This central UDF folder is a legacy feature for older algorithms. For newer algorithms, the UDF files are located in their individual subfolders.

Table of GSQL Graph Algorithms

As of October 5, 2021

Query name Description
tg_adamic_adar Adamic Adar Topoligical Link Prediction
tg_article_rank Article rank
tg_astar A* search
tg_betweenness_cent Betweenness centrality
tg_bfs Breadth-first search
tg_closeness_cent_approx Approximate closeness centrality
tg_closeness_cent Closeness centrality
tg_common_neighbors Common neighbors topological link prediction
tg_cosine_batch Cosine similarity for each pair of vertices, computed in batches
tg_cosine_nbor_ap Cosine similarity for each pair of vertices
tg_cosine_nbor_ss Cosine similarity from a single vertex
tg_cycle_detection Rocha–Thatte algorithm for cycle detection; output the cycles
tg_cycle_detection_count Rocha–Thatte algorithm for cycle detection; output the number of cycles
tg_degree_cent Degree centrality
tg_eigenvector_cent Eigenvector centrality
tg_embedding_cosine_sim One-to-Many embedding cosine similarity
tg_embedding_pairwise_cosine_sim Pairwise embedding cosine similarity
tg_estimate_diameter Heuristic estimate of graph diameter
tg_fastRP FastRP graph embedding
tg_greedy_graph_coloring Greedy graph coloring
tg_harmonic_cent Harmonic centraliity
tg_influence_maximization_CELF Influence maximization using CELF
tg_influence_maximization_greedy Influence maximization using greedy method
tg_jaccard_batch Jaccard similarity for each pair of vertices, computed in batches
tg_jaccard_nbor_ap [1] Jaccard similarity for each pair of vertices
tg_jaccard_nbor_ss [1] Jaccard similarity from a single vertex
tg_kcore K-Core
tg_kmeans K-Means
tg_knn_cosine_all k-Nearest Neighbor classification, using cosine similarity, batch
tg_knn_cosine_cv Cross validation for k-Nearest Neighbor, using cosine similarity
tg_knn_cosine_ss k-Nearest Neighbor classification, using cosine sim., single source
tg_label_prop Label propagation method for community detection
tg_lcc Local clustering coefficient
tg_louvain_distributed Distributed & parallel Louvain Modularity optimzation
tg_louvain_parallel Parallel Louvain Modularity optimization
tg_maxflow Maxflow
tg_maximal_indep_set Maximal independent set
tg_msf Minimum spanning forest (MSF)
tg_mst Minimum spanning tree (MST)
tg_node2vec node2vec graph embedding
tg_pagerank_pers [1] Personalized PageRank
tg_pagerank_wt [1] Weighted PageRank
tg_pagerank [1] PageRank measurement of relative influence of each vertex
tg_preferential_attachment Preferential attachment topological link prediction
tg_random_walk Random walk generator
tg_random_walk_batch Random walk generator, in batches for greater memory efficiency
tg_resource_allocation Resource allocation topological link prediction
tg_same_community Same community topological link prediction
tg_scc Strongly connected component detection
tg_scc_small_world Strongly connected component detection
tg_shortest_ss_any_wt Single-Source shortest paths
tg_shortest_ss_no_wt Single-Source shortest paths without weight
tg_shortest_ss_pos_wt Single-Source shortest paths with positive weight
tg_slpa Speaker-Listener Label Propagation
tg_tri_count_fast Count all the triangles, faster but using more memory
tg_tri_count Count all the triangles, memory effient
tg_total_neighbors Total neighbors topological link prediction
tg_wcc Weakly (undirect) Connected component detection
tg_wcc_small_world Weakly (undirect) Connected component detection
tg_weighted_random_walk Weighted random walk generator
tg_weighted_random_walk_batch Weighted random walk generator, in batches for greater memory efficiency

Notes: [1] The schema-free version of this algorithm can use only one edge type.

The schema-free algorithms ofter all three options in one algorithm.

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

  • Schema-free algorithms:
    1. Change the graph name specified in CREATE statement.
    2. Use the script directly.

More detailed documentation and examples are available on the web at https://docs.tigergraph.com/graph-algorithm-library