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https://github.com/Team309/awesome-graph-processing

A collection of awesome papers about graph processing.
https://github.com/Team309/awesome-graph-processing
List: awesome-graph-processing
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A collection of awesome papers about graph processing.
Host: GitHub
URL: https://github.com/Team309/awesome-graph-processing
Owner: Team309
Created: 2019-03-14T00:55:43.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2019-10-22T00:48:10.000Z (almost 5 years ago)
Last Synced: 2024-05-22T13:01:46.935Z (4 months ago)
Size: 16.6 KB
Stars: 15
Watchers: 5
Forks: 12
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

ultimate-awesome - awesome-graph-processing - A collection of awesome papers about graph processing. (Other Lists / PowerShell Lists)
README

        # Awesome - Graph Processing

A collection of awesome papers about graph processing.

## Categories

### Single & Memory

- **GraphLab** - `GraphLab: A New Framework For Parallel Machine Learning` (UAI'10). [[paper]](https://dslpitt.org/papers/10/p340-low.pdf)

- **Galois** - `A Lightweight Infrastructure for Graph Analytics` (SOSP'13). [[paper]](http://sigops.org/sosp/sosp13/papers/p456-nguyen.pdf)

- **GRACE** - `Asynchronous Large-Scale Graph Processing Made Easy` (CIDR'13). [[paper]](http://www.cs.cornell.edu/~guoz/Guozhang%20Wang%20publications/grace\_cidr2013.pdf)

- **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)

- **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)

- **GraphMat** - `GraphMat: High performance graph analytics made productive` (VLDB'15). [[paper]](https://pdfs.semanticscholar.org/b513/711621e81d0abd042e0877ca751581a993f5.pdf), [[code]](https://github.com/narayanan2004/GraphMat)

- **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)

- **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)

### Distributed & Memory

- **Pregel** - `Pregel: A System for Large-Scale Graph Processing` (SIGMOD'10). [[paper]](https://kowshik.github.io/JPregel/pregel\_paper.pdf)

- **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)

- **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)

- **GPS** - `GPS: A Graph Processing System` (SSDBM'13). [[paper]](http://ilpubs.stanford.edu:8090/1039/7/gps\_ssdbm.pdf)

- **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)

- **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/)

- **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)

- **GraphX** - `GraphX: Graph Processing in a Distributed Dataflow Framework` (OSDI'14). [[paper]](https://www.usenix.org/node/186217), [[code]](https://spark.apache.org/graphx/)

- **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)

- **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)

- **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)

- **GRAPE** - `Parallelizing Sequential Graph Computations` (SIGMOD'17). [[paper]](http://homepages.inf.ed.ac.uk/wenfei/papers/sigmod17-GRAPE.pdf)

### Single & Storage

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

- **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)

### Distributed & Storage

- **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)

- **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)

- **TurboGraph++** - `TurboGraph++: A Scalable and Fast Graph Analytics System` (SIGMOD'18). [[paper]](https://dl.acm.org/citation.cfm?doid=3183713.3196915)

- **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)

### Embedding, Representation Learning, and Deep Learning on Graphs

- **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)

- **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/)

- **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)

- [Must-read papers on NRL/NE](https://github.com/thunlp/NRLPapers)

- [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding)

- [Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding)

- [Deep Learning on Graphs: a roadmap](https://github.com/guillaumejaume/graph-neural-networks-roadmap)

- [Dynamic Knowledge Graph Completion](https://github.com/woojeongjin/dynamic-KG)

- [Graph Neural Network Review](https://github.com/talorwu/Graph-Neural-Network-Review)

## Courses and Tutorials

- [Large Scale Network Analytics with SNAP](http://snap.stanford.edu/proj/snap-www/)

- [Representation Learning on Networks](http://snap.stanford.edu/proj/embeddings-www/)

- [6.886: Graph Analytics at MIT](https://people.csail.mit.edu/jshun/6886-s18/)

## Benckmarks and Challenges

- [The Graph 500 List](http://graph500.org/)

- [The High Performance Conjugate Gradients (HPCG)](http://hpcg-benchmark.org/)

- [GraphChallenge](https://graphchallenge.mit.edu/challenges)

## References

1. [Papers on Graph Aanlytics](https://people.csail.mit.edu/jshun/graph.shtml)

## More Awesome Graph Lists

- [A curated list of awesome network analysis resources](https://github.com/briatte/awesome-network-analysis)

- [A curated list of resources for graph databases and graph computing tools](https://github.com/jbmusso/awesome-graph)

- [Graph Database Acceleration Survey](https://github.com/Liu-Cheng/graph-database-accel-survey)

- [Primitives & Graph Processing](https://github.com/RapidsAtHKUST/PrimitivesAndGraphProcessing-GPU)

- [Awesome Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection)

## License

[![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)](https://creativecommons.org/licenses/by/4.0/)

This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).