Official repository for the paper "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (TPAMI'22) https://arxiv.org/abs/2006.09252

A performant, powerful query framework to search for network motifs

Performant, pure-Python subgraph isomorphism and monomorphism search (aka "motif search")

Neural Subgraph Matching Paper: "AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching". [Neural-Subgraph-Matching Method For Learning-Subgraph-Matching. (Approximate Subgraph matching, SubgraphMatching)]

❄Implement the common subgraph isomorphism algorithms (i.e. Ullmann, VF2) based on MapReduce on Hadoop

The lib CBioInfCpp.h contains 3 groups of functions for C++: "Input-Output", "Working with strings", "Working with graphs". Data structures "Adjacency vector" and "Adjacency map" are implemented in the last one (i.e. in "Working with graphs"). See About_CBioInfCpp for details.

Crystal is a novel method for distributed subgraph matching on very large graphs. Crystal outperforms existing methods by several orders of magnitude on very large graphs. The work was published on VLDB 2018 with title "Subgraph matching: on compression and computation".

Source Code: Sub-GMN: Subgraph Matching Network

A state of the art algorithm for the Graph Isomorphism problem, adjusted for Sub-Graph and Induced Sub-Graph Isomorphism and extended to directed and multigraph settings.

Directed Multigraph Miner (DMGM)

The code implementation of HPEC20 paper *LessMine: Reducing Sample Space and Data Access for Dense Pattern Mining*

Subgraph isomorphism using SAT Solving

A binary and library for subgraph matching in undirected node-labeled graphs.

Implementation of Subgraph isomorphism algorithm and optimization techniques on neo4j graph database

A distributed DucktypeSystem 🦆

C++ (Boost) implementation of Subgraph retrieval from given set of graphs

Flow-a-gorithm is an algorithm-centric framework for detecting anomalies in network traffic through graph-based modeling and subgraph analysis. This project combines subgraph isomorphism, incremental detection algorithms, and a Graph Convolutional Network (GCN) to identify suspicious patterns in dynamic network flow data.

Code and Data for the paper: "Graph Neural Networks Can (Often) Count Substructures" (ICLR 2025 Spotlight)

Running benchmarks (including BI 3/4/10/11/13/14) to compare the performance with or without indexed edges of LDBC-SNB dataset via VEQ algorithm.

subgraph isomorphism based on CPI