{"id":50060,"url":"https://github.com/Team309/awesome-graph-processing","name":"awesome-graph-processing","description":"A collection of awesome papers about graph processing.","projects_count":61,"last_synced_at":"2026-06-10T02:00:20.069Z","repository":{"id":95647905,"uuid":"175522454","full_name":"Team309/awesome-graph-processing","owner":"Team309","description":"A collection of awesome papers about graph processing.","archived":false,"fork":false,"pushed_at":"2019-10-22T00:48:10.000Z","size":17,"stargazers_count":15,"open_issues_count":0,"forks_count":12,"subscribers_count":4,"default_branch":"master","last_synced_at":"2026-05-24T10:04:40.853Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":false,"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/Team309.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}},"created_at":"2019-03-14T00:55:43.000Z","updated_at":"2024-05-06T21:20:37.000Z","dependencies_parsed_at":"2023-04-11T07:47:03.243Z","dependency_job_id":null,"html_url":"https://github.com/Team309/awesome-graph-processing","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Team309/awesome-graph-processing","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Team309%2Fawesome-graph-processing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Team309%2Fawesome-graph-processing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Team309%2Fawesome-graph-processing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Team309%2Fawesome-graph-processing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Team309","download_url":"https://codeload.github.com/Team309/awesome-graph-processing/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Team309%2Fawesome-graph-processing/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34133404,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-10T02:00:07.152Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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"}},"created_at":"2024-01-14T11:15:50.942Z","updated_at":"2026-06-10T02:00:20.070Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["More Awesome Graph Lists","Categories","Courses and Tutorials","Benckmarks and Challenges","References","License"],"sub_categories":["Embedding, Representation Learning, and Deep Learning on Graphs","Single \u0026 Storage","Single \u0026 Memory","Distributed \u0026 Memory","Distributed \u0026 Storage"],"readme":"# Awesome - Graph Processing\nA collection of awesome papers about graph processing.\n\n## Categories\n\n### Single \u0026 Memory\n- **GraphLab** - `GraphLab: A New Framework For Parallel Machine Learning` (UAI'10). [[paper]](https://dslpitt.org/papers/10/p340-low.pdf)\n- **Galois** - `A Lightweight Infrastructure for Graph Analytics` (SOSP'13). [[paper]](http://sigops.org/sosp/sosp13/papers/p456-nguyen.pdf)\n- **GRACE** - `Asynchronous Large-Scale Graph Processing Made Easy` (CIDR'13). [[paper]](http://www.cs.cornell.edu/~guoz/Guozhang%20Wang%20publications/grace\\_cidr2013.pdf)\n- **Ligra** - `Ligra: A Lightweight Graph Processing Framework for Shared Memory` (PPoPP'13). [[paper]](https://www.cs.cmu.edu/~jshun/ligra.pdf), [[code]](https://github.com/jshun/ligra)\n- **Polymer** - `NUMA-Aware Graph-Structured Analytics` (PPoPP'15). [[paper]](https://people.csail.mit.edu/jshun/6886-s18/papers/Polymer.pdf), [[code]](https://github.com/realstolz/polymer)\n- **GraphMat** - `GraphMat: High performance graph analytics made productive` (VLDB'15). [[paper]](https://pdfs.semanticscholar.org/b513/711621e81d0abd042e0877ca751581a993f5.pdf), [[code]](https://github.com/narayanan2004/GraphMat)\n- **Graph Ordering** - `Speedup Graph Processing by Graph Ordering` (SIGMOD'16). [[paper]](https://dl.acm.org/citation.cfm?id=2915220), [[slides]](https://people.csail.mit.edu/jshun/6886-s18/lectures/lecture13-3.pdf), [[code]](https://github.com/datourat/Gorder)\n- **GPOP** - `GPOP: A cache and memory-efficient framework for Graph Processing Over Partitions` (PPoPP'19 *poster*). [[poster]](https://ppopp19.sigplan.org/event/ppopp-2019-posters-poster-gpop-a-cache-and-memory-efficient-framework-for-graph-processing-over-partitions), [[arxiv]](https://arxiv.org/abs/1806.08092v1), [[code]](https://github.com/souravpati/GPOP)\n\n### Distributed \u0026 Memory\n- **Pregel** - `Pregel: A System for Large-Scale Graph Processing` (SIGMOD'10). [[paper]](https://kowshik.github.io/JPregel/pregel\\_paper.pdf)\n- **Distributed GraphLab** - `Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud` (VLDB'12). [[paper]](http://vldb.org/pvldb/vol5/p716\\_yuchenglow\\_vldb2012.pdf)\n- **PowerGraph** - `PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs` (OSDI'12). [[paper]](https://www.usenix.org/system/files/conference/osdi12/osdi12-final-167.pdf), [[code]](https://github.com/jegonzal/PowerGraph)\n- **GPS** - `GPS: A Graph Processing System` (SSDBM'13). [[paper]](http://ilpubs.stanford.edu:8090/1039/7/gps\\_ssdbm.pdf)\n- **Mizan** - `Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing` (EuroSys'13). [[paper]](http://www.cs.cornell.edu/~djwill/pubs/mizan.pdf), [[code]](https://github.com/khayyatzy/Mizan)\n- **Blogel** - `Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs` (VLDB'14). [[paper]](http://people.csail.mit.edu/yilu/papers/p1981-yan.pdf), [[code]](http://www.cse.cuhk.edu.hk/blogel/)\n- **Giraph++** - `From \"Think Like a Vertex\" to \"Think Like a Graph\"` (VLDB'14). [[paper]](https://researcher.watson.ibm.com/researcher/files/us-ytian/giraph++.pdf)\n- **GraphX** - `GraphX: Graph Processing in a Distributed Dataflow Framework` (OSDI'14). [[paper]](https://www.usenix.org/node/186217), [[code]](https://spark.apache.org/graphx/)\n- **PowerLyra** - `PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs` (EuroSys'15). [[paper]](https://ipads.se.sjtu.edu.cn/lib/exe/fetch.php?media=publications:powerlyra-eurosys15.pdf), [[code]](https://github.com/realstolz/powerlyra)\n- **PowerSwith** - `SYNC or ASYNC: Time to Fuse for Distributed Graph-Parallel Computation` (PPoPP'15). [[paper]](https://ipads.se.sjtu.edu.cn/\\_media/publications/powerswitch-ppopp15.pdf)\n- **Gemini** - `Gemini: A Computation-Centric Distributed Graph Processing System` (OSDI'16). [[paper]](https://www.usenix.org/system/files/conference/osdi16/osdi16-zhu.pdf), [[code]](https://github.com/thu-pacman/GeminiGraph)\n- **GRAPE** - `Parallelizing Sequential Graph Computations` (SIGMOD'17). [[paper]](http://homepages.inf.ed.ac.uk/wenfei/papers/sigmod17-GRAPE.pdf)\n\n### Single \u0026 Storage\n- **GraphChi** - `GraphChi: Large-Scale Graph Computation on Just a PC` (OSDI'12). [[paper]](https://www.usenix.org/system/files/conference/osdi12/osdi12-final-126.pdf), [[code]](https://github.com/GraphChi/graphchi-cpp)\n- **X-Stream** - `X-Stream: Edge-centric Graph Processing using Streaming Partitions` (SOSP'13). [[paper]](https://infoscience.epfl.ch/record/188535/files/paper.pdf), [[code]](https://github.com/epfl-labos/x-stream)\n- **TurboGraph** - `TurboGraph: A Fast Parallel Graph Engine Handling Billion-scale Graphs in a Single PC` (KDD'13). [[paper]](http://www.eiti.uottawa.ca/~nat/Courses/csi5387\\_Winter2014/paper1.pdf)\n- **PathGraph** - `Fast Iterative Graph Computation: A Path Centric Approach` (SC'14). [[paper]](https://people.csail.mit.edu/jshun/6886-s18/papers/PathGraph.pdf), [[code]](https://github.com/CGCL-codes/PathGraph)\n- **GridGraph** - `GridGraph: Large-Scale Graph Processing on a Single Machine Using 2-Level Hierarchical Partitioning` (USENIX ATC'15). [[paper]](https://www.usenix.org/system/files/conference/atc15/atc15-paper-zhu.pdf), [[code]](https://github.com/thu-pacman/GridGraph)\n- **VENUS** - `VENUS: Vertex-Centric Streamlined Graph Computation on a Single PC` (ICDE'15). [[paper]](https://www.cse.cuhk.edu.hk/~cslui/PUBLICATION/ICDE15\\_Venus.pdf)\n- **FlashGraph** - `FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs` (FAST'15). [[paper]](https://www.usenix.org/system/files/conference/fast15/fast15-paper-zheng.pdf), [[code]](https://github.com/flashxio/FlashX)\n- **Dynamic Shards** - `Load the Edges You Need: A Generic I/O Optimization for Disk-based Graph Processing` (ATC'16). [[paper]](https://www.usenix.org/system/files/conference/atc16/atc16_paper-vora.pdf), [[slides]](https://www.usenix.org/sites/default/files/conference/protected-files/atc16_slides_vora.pdf), [[similar code]](https://github.com/kevalvora/omr)\n- **Graphene** - `Graphene: Fine-Grained IO Management for Graph Computing` (FAST'17). [[paper]](https://www.usenix.org/system/files/conference/fast17/fast17-liu.pdf), [[slides]](https://www.usenix.org/sites/default/files/conference/protected-files/fast17\\_slides\\_liu.pdf), [[code]](https://github.com/iHeartGraph/Graphene)\n- **Mosaic**  - `Mosaic: Processing a Trillion-Edge Graph on a Single Machine` (EuroSys'17). [[paper]](https://taesoo.kim/pubs/2017/maass:mosaic.pdf), [[slides]](https://taesoo.kim/pubs/2017/maass:mosaic-slides.pdf), [[code]](https://github.com/sslab-gatech/mosaic)\n- **pre-processing trade-off** - `Everything you always wanted to know about multicore graph processing but were afraid to ask` (ATC'17). [[paper]](https://www.usenix.org/system/files/conference/atc17/atc17-malicevic.pdf), [[slides]](https://www.usenix.org/sites/default/files/conference/protected-files/atc17_slides_malicevic.pdf), [[code]](https://github.com/epfl-labos/EverythingGraph.git)\n- **CLIP** - `Squeezing out All the Value of Loaded Data: An Out-of-core Graph Processing System with Reduced Disk I/O` (ATC'17). [[paper]](https://www.usenix.org/system/files/conference/atc17/atc17-ai.pdf), [[slides]](https://www.usenix.org/sites/default/files/conference/protected-files/atc17_slides_ai.pdf), [[author]](https://github.com/james0zan)\n- **GraFBoost** - `GraFBoost: Accelerated Flash Storage for External Graph Analytics` (ISCA'18). [[paper]](http://people.csail.mit.edu/wjun/papers/isca2018-camera.pdf), [[code]](https://github.com/sangwoojun/sortreduce)\n- **GraphOne** - `GraphOne: A Data Store for Real-time Analytics on Evolving Graphs` (FAST'19). [[paper]](https://www.usenix.org/conference/fast19/presentation/kumar), [[code]](https://github.com/pradeep-k/GraphOne)\n\n### Distributed \u0026 Storage\n- **Chaos** - `Chaos: Scale-out Graph Processing from Secondary Storage` (SOSP'15). [[paper]](https://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244\\_2017\\_2018/papers/roy\\_sosp\\_2015.pdf), [[code]](https://github.com/epfl-labos/chaos)\n- **Pregelix** - `Pregelix: Big(ger) Graph Analytics on A Dataflow Engine` (VLDB'15). [[paper]](http://www.vldb.org/pvldb/vol8/p161-bu.pdf), [[code]](https://github.com/pregelix/pregelix)\n- **TurboGraph++** - `TurboGraph++: A Scalable and Fast Graph Analytics System` (SIGMOD'18). [[paper]](https://dl.acm.org/citation.cfm?doid=3183713.3196915)\n- **GraphD** - `GraphD: Distributed Vertex-Centric Graph Processing Beyond the Memory Limit` (TPDS'18). [[paper]](https://ieeexplore.ieee.org/document/8016377), [[code]](https://github.com/yaobaiwei/GraphD)\n\n### Embedding, Representation Learning, and Deep Learning on Graphs\n\n- **graph2vec** - `graph2vec: Learning Distributed Representations of Graphs` ([MLG](http://www.mlgworkshop.org)'17, Held in conjunction with KDD). [[paper]](http://www.mlgworkshop.org/2017/paper/MLG2017_paper_21.pdf), [[arxiv]](https://arxiv.org/abs/1707.05005v1), [[code]](https://github.com/benedekrozemberczki/graph2vec)\n- **GraphSage** - `GraphSage: Representation Learning on Large Graphs` (NIPS'17). [[paper]](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf), [[arxiv]](https://arxiv.org/pdf/1706.02216.pdf), [[code]](https://github.com/williamleif/GraphSAGE), [[pytorch version]](https://github.com/williamleif/graphsage-simple/), [[project]](http://snap.stanford.edu/graphsage/)\n- **graph2gauss** - `Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking` (ICLR'18). [[paper]](https://openreview.net/forum?id=r1ZdKJ-0W), [[code]](https://github.com/abojchevski/graph2gauss)\n- [Must-read papers on NRL/NE](https://github.com/thunlp/NRLPapers)\n- [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding)\n- [Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding)\n- [Deep Learning on Graphs: a roadmap](https://github.com/guillaumejaume/graph-neural-networks-roadmap)\n- [Dynamic Knowledge Graph Completion](https://github.com/woojeongjin/dynamic-KG)\n- [Graph Neural Network Review](https://github.com/talorwu/Graph-Neural-Network-Review)\n\n## Courses and Tutorials\n\n- [Large Scale Network Analytics with SNAP](http://snap.stanford.edu/proj/snap-www/)\n- [Representation Learning on Networks](http://snap.stanford.edu/proj/embeddings-www/)\n- [6.886: Graph Analytics at MIT](https://people.csail.mit.edu/jshun/6886-s18/)\n\n## Benckmarks and Challenges\n\n- [The Graph 500 List](http://graph500.org/)\n- [The High Performance Conjugate Gradients (HPCG)](http://hpcg-benchmark.org/)\n- [GraphChallenge](https://graphchallenge.mit.edu/challenges)\n\n## References\n\n1. [Papers on Graph Aanlytics](https://people.csail.mit.edu/jshun/graph.shtml)\n\n## More Awesome Graph Lists\n\n- [A curated list of awesome network analysis resources](https://github.com/briatte/awesome-network-analysis)\n- [A curated list of resources for graph databases and graph computing tools](https://github.com/jbmusso/awesome-graph)\n- [Graph Database Acceleration Survey](https://github.com/Liu-Cheng/graph-database-accel-survey)\n- [Primitives \u0026 Graph Processing](https://github.com/RapidsAtHKUST/PrimitivesAndGraphProcessing-GPU)\n- [Awesome Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection)\n\n## License\n\n[![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)](https://creativecommons.org/licenses/by/4.0/)\n\nThis work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/team309%2Fawesome-graph-processing/projects"}