{"id":18244259,"url":"https://github.com/manikantasanjay/cs257_subgraph_matching","last_synced_at":"2026-02-21T10:02:47.148Z","repository":{"id":211358058,"uuid":"728915237","full_name":"ManikantaSanjay/CS257_Subgraph_Matching","owner":"ManikantaSanjay","description":"in-depth analysis and comparison of eight leading in-memory subgraph matching algorithms, QuickSI, GraphQL, CFL, CECI, DP-iso, RI, and VF2++.","archived":false,"fork":false,"pushed_at":"2023-12-09T05:05:01.000Z","size":9811,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-19T12:26:33.978Z","etag":null,"topics":["graph-processing","in-memory-computing","optimization-techniques","performance-benchmarking","subgraph-matching"],"latest_commit_sha":null,"homepage":"","language":"Makefile","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ManikantaSanjay.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-12-08T01:21:47.000Z","updated_at":"2024-04-25T04:58:22.000Z","dependencies_parsed_at":"2025-02-14T14:47:21.805Z","dependency_job_id":"f49f1383-9c27-450f-b2ed-5e0f807d7623","html_url":"https://github.com/ManikantaSanjay/CS257_Subgraph_Matching","commit_stats":null,"previous_names":["manikantasanjay/cs257_subgraph_matching"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ManikantaSanjay/CS257_Subgraph_Matching","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FCS257_Subgraph_Matching","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FCS257_Subgraph_Matching/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FCS257_Subgraph_Matching/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FCS257_Subgraph_Matching/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ManikantaSanjay","download_url":"https://codeload.github.com/ManikantaSanjay/CS257_Subgraph_Matching/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FCS257_Subgraph_Matching/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29679049,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-21T09:33:50.764Z","status":"ssl_error","status_checked_at":"2026-02-21T09:33:19.949Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["graph-processing","in-memory-computing","optimization-techniques","performance-benchmarking","subgraph-matching"],"created_at":"2024-11-05T09:15:56.003Z","updated_at":"2026-02-21T10:02:47.130Z","avatar_url":"https://github.com/ManikantaSanjay.png","language":"Makefile","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Subgraph Matching\n## Introduction\nThis project analyzes eight leading in-memory subgraph matching algorithms, including QuickSI, GraphQL, CFL, CECI, DP-iso, RI, and VF2++. We evaluate them based on:\n1. Vertex filtering methods in data graphs.\n2. Techniques for ordering query vertices.\n3. Strategies for enumerating partial results.\n4. Other optimization approaches.\n\nWe also compare these algorithms against Glasgow, a constraint programming-based algorithm. Our findings show the strengths of different methods in various aspects, leading to recommendations for specific scenarios based on graph density and query size.\n\n## Compilation\nTo compile the source code, navigate to the project's root directory and execute:\n\n```zsh\nmkdir build\ncd build\ncmake ..\nmake\n```\n\n## Testing\nTo verify the binary's correctness, run the following:\n\n```zsh\ncd test\npython test.py ../build/matching/SubgraphMatching.out\n```\n\n## Execution\nFind the compiled 'SubgraphMatching.out' in 'build/matching'. Execute it using:\n\n```zsh\n./SubgraphMatching.out -d [data_graphs] -q [query_graphs] -filter [filter_method] -order [order_method] -engine [enum_method] -num [embeddings_number]\n```\n\nSet `-num` to 'MAX' for all results. The program stops upon reaching the limit or finding all results.\n\nExample:\n\n```zsh\n./SubgraphMatching.out -d ../../test/sample_dataset/test_case_1.graph -q ../../test/sample_dataset/query1_positive.graph -filter GQL -order GQL -engine LFTJ -num MAX\n```\n\n## Input Format\nThe input graphs should be vertex-labeled, starting with 't N M' (N = vertices, M = edges). Format vertices as 'v VertexID LabelId Degree' and edges as 'e VertexId VertexId'. Vertex ids start from 0.\n\nExample:\n\n```zsh\nt 5 6\nv 0 0 2\nv 1 1 3\nv 2 2 3\nv 3 1 2\nv 4 2 2\ne 0 1\ne 0 2\ne 1 2\ne 1 3\ne 2 4\ne 3 4\n```\n\n## Supported Techniques\n### Filtering Methods\n- LDF: Label and degree filter (default).\n- NLF: Neighborhood label frequency filter.\n- GQL: GraphQL's method.\n- TSO: TurboIso's method.\n- CFL: CFL's method.\n- DPiso: DP-iso's method.\n- CECI: CECI's method.\n\n### Ordering Methods\n- QSI: QuickSI's method.\n- GQL: GraphQL's method.\n- TSO: TurboIso's method.\n- CFL: CFL's method.\n- DPiso: DP-iso's method.\n- CECI: CECI's method.\n- RI: RI's method.\n- VF2++: VF2++'s method.\n\n### Enumeration Methods\n- QSI: QuickSI and RI's method.\n- GQL: GraphQL's method.\n- EXPLORE: CFL's method.\n- DPiso: DP-iso's method.\n- CECI: CECI's method.\n- LFTJ: Local candidates computation.\n\nExamples of command-line execution are provided for various configurations.\n\n## Recommendations\nWe suggest using GQL, CFL, or DPiso for filtering; GQL or RI for ordering; and always LFTJ for enumeration. Enable failing set pruning for large queries and QFilter for dense graphs.\n\n## Command-Line Execution Examples\n\n### Basic Execution\nRun a basic query using GraphQL's filtering and ordering methods with local candidate computation for enumeration:\n\n```zsh\n./SubgraphMatching.out -d ../../test/sample_dataset/test_case_1.graph -q ../../test/sample_dataset/query1_positive.graph -filter GQL -order GQL -engine LFTJ -num MAX\n```\n\n### Custom Execution\nExecute with custom settings, such as using label and degree filter, CFL's ordering method, and set-intersection based local candidates computation:\n\n```zsh\n./SubgraphMatching.out -d ../../test/sample_dataset/test_case_1.graph -q ../../test/sample_dataset/query1_positive.graph -filter LDF -order CFL -engine LFTJ -num MAX\n```\n\n### Advanced Configuration\nFor complex queries, configure the set intersection algorithms and optimization techniques in 'configuration/config.h'.\n\n### Spectrum Analysis\nPerform spectrum analysis on a query with time limits:\n\n```zsh\n./SubgraphMatching.out -d ../../test/sample_dataset/test_case_1.graph -q ../../test/sample_dataset/query1_positive.graph -filter LDF -order Spectrum -engine Spectrum -order_num 100 -time_limit 60 -num 100000\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanikantasanjay%2Fcs257_subgraph_matching","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmanikantasanjay%2Fcs257_subgraph_matching","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanikantasanjay%2Fcs257_subgraph_matching/lists"}