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https://github.com/zhiningliu1998/graph-papers
Personal paper reading list
https://github.com/zhiningliu1998/graph-papers
Last synced: 18 days ago
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Personal paper reading list
- Host: GitHub
- URL: https://github.com/zhiningliu1998/graph-papers
- Owner: ZhiningLiu1998
- License: mit
- Created: 2022-09-06T02:57:03.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-04-26T22:07:00.000Z (over 1 year ago)
- Last Synced: 2024-10-28T15:26:27.463Z (2 months ago)
- Size: 8.79 KB
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
> Template: | ConfName | PaperTitle | [[pdf]()] [[code]()] |
Contents
- [Fair Graph Mining](#fair-graph-mining)
- [Degree-related bias](#degree-related-bias)
- [Class-related bias](#class-related-bias)
- [Supervision-related bias](#supervision-related-bias)
- [Graph Learning with Weak Supervision](#graph-learning-with-weak-supervision)
- [Graph Neural Network Architecture](#graph-neural-network-architecture)
- [Graph Anomaly Detection](#graph-anomaly-detection)
- [Graph Data Augmentation](#graph-data-augmentation)
- [Graph Uncertainty](#graph-uncertainty)
- [Graph Contrastive Learning](#graph-contrastive-learning)
- [Spatio-temporal Graph Mining](#spatio-temporal-graph-mining)
## Fair Graph Mining
### Degree-related bias
| Venue | Title | Links |
| ------- | ---------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
| WWW'22 | Rawlsgcn: Towards rawlsian difference principle on graph convolutional network | [[pdf](https://arxiv.org/pdf/2202.13547v1.pdf)] [[code](https://github.com/jiank2/RawlsGCN)] |
| CIKM'20 | Investigating and mitigating degree-related biases in graph convoltuional networks | [[pdf](https://dl.acm.org/doi/pdf/10.1145/3340531.3411872)] |
### Class-related bias
| Venue | Title | Links |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------ |
| ICML'22 | TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification | [[pdf](https://proceedings.mlr.press/v162/song22a/song22a.pdf)] [[code](https://github.com/Jaeyun-Song/TAM)] |
| ICLR'22 | GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification | [[pdf](https://openreview.net/pdf?id=MXEl7i-iru)] [[code](https://github.com/JoonHyung-Park/GraphENS)] |
| CIKM'22 | LTE4G: Long-Tail Experts for Graph Neural Networks | [[pdf](https://arxiv.org/pdf/2208.10205.pdf)] [[code](https://github.com/SukwonYun/LTE4G)] |
| arXiv'21 | GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction | [[pdf](https://arxiv.org/pdf/2106.11133v1.pdf)] |
| NeurIPS'21 | Topology-Imbalance Learning for Semi-Supervised Node Classification [[中文博客](https://zhuanlan.zhihu.com/p/561261334)] | [[pdf](https://arxiv.org/pdf/2110.04099v1.pdf)] [[code](https://github.com/victorchen96/renode)] |
| WSDM'21 | GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks [[中文博客](https://zhuanlan.zhihu.com/p/561260176)] | [[pdf](https://arxiv.org/pdf/2103.08826v1.pdf)] [[code](https://github.com/TianxiangZhao/GraphSmote)] |
| IJCAI'20 | Multi-Class Imbalanced Graph Convolutional Network Learning | [[pdf](https://par.nsf.gov/servlets/purl/10199469)] |
| J MIA | RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data | [[pdf](https://arxiv.org/pdf/2103.00221v3.pdf)] |
### Supervision-related bias
| Venue | Title | Links |
| ---------- | ------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| NeurIPS'21 | Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data | [[pdf](https://proceedings.neurips.cc/paper/2021/file/eb55e369affa90f77dd7dc9e2cd33b16-Paper.pdf)] [[code](https://github.com/GentleZhu/Shift-Robust-GNNs)] |
## Graph Learning with Weak Supervision
- https://github.com/kaize0409/awesome-few-shot-gnn
| Venue | Title | Links |
| ------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- |
| AAAI'20 | Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes | [[pdf](https://zhouchenlin.github.io/Publications/2020-AAAI-M3S.pdf)] |
| arXiv | Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity | [[pdf](https://arxiv.org/pdf/2208.12422.pdf)] |
| WSDM'23 | Few-shot Node Classification with Extremely Weak Supervision | [[pdf](https://arxiv.org/pdf/2301.02708.pdf)] [[code](https://github.com/SongW-SW/X-FNC)] |
| ICDM'22 | Generalized Few-Shot Node Classification | [[pdf](https://www.public.asu.edu/~kding9/pdf/Zhe_FewShotClassification_ICDM22.pdf)] [[code](https://github.com/pricexu/STAGER)] |
## Graph Neural Network Architecture
| Venue | Title | Links |
| ------- | ---------------------------------------- | --------------------------------------------------------- |
| ICML'19 | Simplifying Graph Convolutional Networks | [[pdf](http://proceedings.mlr.press/v97/wu19e/wu19e.pdf)] |
## Graph Anomaly Detection
| Venue | Title | Links |
| ------- | ----------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| TKDE'21 | A Comprehensive Survey on Graph Anomaly Detection with Deep Learning | [[pdf](https://arxiv.org/pdf/2106.07178v5.pdf)] [[code](https://github.com/XiaoxiaoMa-MQ/Awesome-Deep-Graph-Anomaly-Detection)] |
| ICDM'21 | FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance | [[pdf](https://ieeexplore.ieee.org/iel7/9678506/9678989/09679178.pdf?casa_token=u388Zhq6ExwAAAAA:nzpXPJ8mfR45579H4tyiGAjlfxSrh2LukB9BRQYXKBNzMIGe0RAAJZNfQ5mnUT-0W3vKOJin-A)] [[code](https://github.com/GeZhangMQ/FRAUDRE_)] |
| WWW'21 | Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection | [[pdf](https://dl.acm.org/doi/pdf/10.1145/3442381.3449989)] [[code](https://github.com/PonderLY/PC-GNN)] |
| WWW'21 | Few-shot Network Anomaly Detection via Cross-network Meta-learning | [[pdf](https://arxiv.org/pdf/2102.11165v1.pdf)] [[code](https://github.com/kaize0409/Meta-GDN_AnomalyDetection)] |
## Graph Data Augmentation
- https://github.com/kaize0409/awesome-graph-data-augmentaion
| Venue | Title | Links |
| -------- | -------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| arXiv'22 | Graph Data Augmentation for Graph Machine Learning: A Survey | [[pdf](https://arxiv.org/pdf/2202.08871v1.pdf)] [[code](https://github.com/zhao-tong/graph-data-augmentation-papers)] |
| KDD'22 | COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning | [[pdf](https://arxiv.org/pdf/2206.04726v2.pdf)] [[code](https://github.com/yifeiacc/COSTA)] |
## Graph Uncertainty
| Venue | Title | Links |
| ------ | -------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| KDD'22 | JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks | [[pdf](https://dl.acm.org/doi/pdf/10.1145/3534678.3539286?casa_token=AxaBEu59ZI4AAAAA:2sV37drJUdBybc9z1mCnh3YZMD9MMUBlqRVTrTZTOaTKQHGLySycFjSFLElglFqWpI_talPfJEzp)] |
## Graph Contrastive Learning
| Venue | Title | Links |
| -------- | --------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
| IJCAI'21 | CuCo: Graph Representation with Curriculum Contrastive Learning | [[pdf](https://www.ijcai.org/proceedings/2021/0317.pdf)] [[code](https://github.com/BUPT-GAMMA/CuCo)] |
## Spatio-temporal Graph Mining
| Venue | Title | Links |
| ---------------- | ------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------- |
| CSUR'18 (Survey) | Spatio-temporal data mining: A survey of problems and methods | [[pdf](https://dl.acm.org/doi/pdf/10.1145/3161602)] |
| CIKM'21 (Survey) | DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction | [[pdf](https://arxiv.org/pdf/2108.09091v1.pdf)] [[code](https://github.com/deepkashiwa20/dl-traff-graph)] |
| AAAI'21 | Hierarchical Graph Convolution Network for Traffic Forecasting | [[pdf](https://ojs.aaai.org/index.php/AAAI/article/view/16088/15895)] |
| AAAI'21 | Spatial-temporal fusion graph neural networks for traffic flow forecasting | [[pdf](https://arxiv.org/pdf/2012.09641v2.pdf)] [[code](https://github.com/MengzhangLI/STFGNN)] |
| WWW'21 | Network of Tensor Time Series | [[pdf](https://arxiv.org/pdf/2102.07736v3.pdf)] [[code](https://github.com/baoyujing/NET3)] |
| IJCAI'18 | STGCN: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting | [[pdf](https://arxiv.org/pdf/1709.04875v4.pdf)] [[code](https://github.com/VeritasYin/STGCN_IJCAI-18)] |
| ICLR'18 | DCRNN: Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting | [[pdf](https://arxiv.org/pdf/1707.01926v3.pdf)] [[code](https://github.com/liyaguang/DCRNN)] |