In-Memory Subgraph Matching: An In-depth Study by Dr. Shixuan Sun and Prof. Qiong Luo

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

Source code and datasets of "An In-Depth Study of Continuous Subgraph Matching", accepted by VLDB'22 - By Xibo Sun, Dr. Shixuan Sun, Prof. Qiong Luo, and Prof. Bingsheng He

Source code of "RapidFlow: An Efficient Approach to Continuous Subgraph Matching" published in VLDB'2022 - By Shixuan Sun, Xibo Sun, Bingsheng He and Qiong Luo

Source code and datasets of "Efficient GPU-Accelerated Subgraph Matching", accepted by SIGMOD'23 - By Xibo Sun and Prof. Qiong Luo

The repo lists papers of various areas, including Graph Analytics, Graph Acceleration, Subgraph Matching, Streaming Graph Management, Worst-case Optimal Join Algorithms, Join Plan Optimization, and so on.

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".

Must-read papers on streaming graph

Source Code: Sub-GMN: Subgraph Matching Network

To query a pattern on multi directed graph with label / attribute filtering efficiently, support Neo4j and SQLite3 as the storage layer.

Approximate Subgraph Matching on Large, Dynamic Graphs.

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

This repository contains a JavaScript algorithm designed to compare two graphs and identify the largest subgraph while calculating its critical path. Developed as a project for a course in my master's program, this project delves into discrete structures and graph theory, offering practical insights into graph analysis and optimization

This is the code of "Locality Sensitive Hashing for Optimizing Subgraph Query Processing in Parallel Computing System".

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.

子图匹配，或者说图计算算法的相关代码

Distributed Subgraph Enumeration via Backtracking-based Framework

Tool for generating RDFS/OWL ontologies and knowledge graphs. It helps to test AI pipelines by creating synthetic data with reliable knowledge structure and characteristic graph patterns. It also aims to advance the topic of generating realistic RDF knowledge graphs through parametric generation and subgraph matching techniques.

Python and LaTeX code for my master's thesis on the topic "Complexity and Algorithms for Weighted Subgraphs"