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https://github.com/LFD-byte/awesome-influencial-graph-papers

A collection of research papers on graph.
https://github.com/LFD-byte/awesome-influencial-graph-papers
List: awesome-influencial-graph-papers
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A collection of research papers on graph.
Host: GitHub
URL: https://github.com/LFD-byte/awesome-influencial-graph-papers
Owner: LFD-byte
License: apache-2.0
Created: 2023-12-07T01:30:45.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-11-22T10:53:52.000Z (29 days ago)
Last Synced: 2024-11-22T11:27:39.864Z (29 days ago)
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Stars: 30
Watchers: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE
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ultimate-awesome - awesome-influencial-graph-papers - A collection of research papers on graph. (Other Lists / Monkey C Lists)
README

        # awesome-graph-papers

A collection of research papers on graph.

## GCN

这里整理了一些图卷积文章，参考了 [pytorch-geometric](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#pooling-layers) 和 [dgl](https://docs.dgl.ai/api/python/nn-pytorch.html#global-pooling-layers) 中的图卷积函数。

- Semi-Supervised Classification with Graph Convolutional Networks `GCNConv` `ICLR 2017`[[pdf]](https://arxiv.org/pdf/1609.02907.pdf) [[code]](https://github.com/tkipf/pygcn)

- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering `ChebConv` `NIPS 2016` [[pdf]](https://arxiv.org/pdf/1606.09375.pdf)

- Inductive Representation Learning on Large Graphs `SAGEConv` `NIPS 2017` [[pdf]](https://arxiv.org/pdf/1706.02216.pdf)

- Inductive Representation Learning on Large Graphs `CuGraphSAGEConv` `NIPS 2017` [[pdf]](https://arxiv.org/pdf/1706.02216.pdf)

- Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks `GraphConv` `AAAI 2019` [[pdf]](https://arxiv.org/pdf/1810.02244.pdf)

- Learning Representations of Irregular Particle-detector Geometry with Distance-weighted Graph Networks `GravNetConv` `EPJ C 2019` [[pdf]](https://arxiv.org/pdf/1902.07987.pdf)

- Gated Graph Sequence Neural Networks `GatedGraphConv` `ICLR 2016` [[pdf]](https://arxiv.org/pdf/1511.05493.pdf)

- Residual Gated Graph ConvNets `ResGateGraphConv` `ICLR 2018` [[pdf]](https://arxiv.org/pdf/1711.07553.pdf)

- Graph Attention Networks `GATConv` `ICLR 2018` [[pdf]](https://arxiv.org/pdf/1710.10903.pdf)

- Graph Attention Networks `CuGraphGATConv` `ICLR 2018` [[pdf]](https://arxiv.org/pdf/1710.10903.pdf)

- Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective `FusedGATConv` `MLSys 2022` [[pdf]](https://arxiv.org/abs/2110.09524)

- How Powerful are Graph Neural Networks? `GATv2Conv` `ICLR 2019` [[pdf]](https://arxiv.org/pdf/1810.00826.pdf)

- Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification `TransformerConv` `IJCAI 2021` [[pdf]](https://arxiv.org/pdf/2009.03509.pdf)

- Attention-based Graph Neural Network for Semi-Supervised Learning `AGNNConv` `ICLR 2018` [[pdf]](https://arxiv.org/pdf/1803.03735.pdf)

- Topology Adaptive Graph Convolutional Networks `TAGConv` `arxiv 2017` [[pdf]](https://arxiv.org/pdf/1710.10370.pdf)

- How Powerful are Graph Neural Networks? `GINConv` `ICLR 2019` [[pdf]](https://arxiv.org/pdf/1810.00826.pdf)

- Strategies for Pre-training Graph Neural Networks `GINEConv` `ICLR 2020` [[pdf]](https://arxiv.org/pdf/1905.12265.pdf)

- Graph Neural Networks with Convolutional ARMA Filters `ARMAConv` `IEEE Trans 2022` [[pdf]](https://arxiv.org/pdf/1901.01343.pdf)

- Simplifying Graph Convolutional Networks `SGConv` `ICML 2019` [[pdf]](https://arxiv.org/pdf/1902.07153.pdf)

- Simple Spectral Graph Convolution `SSGConv` `ICLR 2021` [[pdf]](https://openreview.net/pdf?id=CYO5T-YjWZV)

- Predict then Propagate: Graph Neural Networks meet Personalized PageRank `APPNPConv` `ICLR 2019` [[pdf]](https://arxiv.org/pdf/1810.05997.pdf)

- Convolutional Networks on Graphs for Learning Molecular Fingerprints `MFConv` `NIPS 2015` [[pdf]](https://arxiv.org/pdf/1509.09292.pdf)

- Modeling Relational Data with Graph Convolutional Networks `RGCNConv` `ESWC 2018` [[pdf]](https://arxiv.org/pdf/1703.06103.pdf)

- Modeling Relational Data with Graph Convolutional Networks `FastRGCNConv` `ESWC 2018` [[pdf]](https://arxiv.org/pdf/1703.06103.pdf)

- Modeling Relational Data with Graph Convolutional Networks `CuGraphRGCNConv` `ESWC 2018` [[pdf]](https://arxiv.org/pdf/1703.06103.pdf)

- Relational Graph Attention Networks `RGAConv` `arxiv 2019` [[pdf]](https://arxiv.org/abs/1904.05811)

- Signed Graph Convolutional Network `SignedConv` `ICDM 2018` [[pdf]](https://arxiv.org/pdf/1808.06354.pdf)

- Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks `DNAConv` `arxiv 2019` [[pdf]](https://arxiv.org/abs/1904.04849)

- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation `PointConv` `CVPR 2017` [[pdf]](https://arxiv.org/pdf/1612.00593.pdf)

- Dynamic Graph CNN for Learning on Point Clouds `EdgeConv` `ACM Trans 2019` [[pdf]](https://arxiv.org/pdf/1801.07829.pdf)

- PointCNN: Convolution On X-Transformed Points `XConv` `NIPS 2018` [[pdf]](https://arxiv.org/pdf/1801.07791.pdf)

- PPFNet: Global Context Aware Local Features for Robust 3D Point Matching `PPFConv` `CVPR 2018` [[pdf]](https://arxiv.org/pdf/1802.02669.pdf)

- FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis `FeaStConv` `CVPR 2018` [[pdf]](https://arxiv.org/abs/1706.05206)

- Point Transformer `PointTransformerConv` `ICCV 2021` [[pdf]](https://arxiv.org/abs/2012.09164)

- Hypergraph Convolution and Hypergraph Attention `HyperGraphConv` `Pattern Recognit 2021` [[pdf]](https://arxiv.org/abs/1901.08150)

- ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations `LEConv` `AAAI 2020` [[pdf]](https://arxiv.org/abs/1911.07979)

- Principal Neighbourhood Aggregation for Graph Nets `PNAConv` `NeurIPS 2020` [[pdf]](https://arxiv.org/pdf/2004.05718.pdf)

- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks `ClusterConv` `KDD 2019` [[pdf]](https://arxiv.org/pdf/1905.07953.pdf)

- DeeperGCN: All You Need to Train Deeper GCNs `GENConv` `arxiv 2020` [[pdf]](https://arxiv.org/pdf/2006.07739.pdf)

- Simple and Deep Graph Convolutional Networks `GCN2Conv` `ICML 2020` [[pdf]](https://arxiv.org/pdf/2007.02133.pdf)

- Path Integral Based Convolution and Pooling for Graph Neural Networks `PANConv` `NIPS 2020` [[pdf]](https://arxiv.org/pdf/2006.16811.pdf)

- A Reduction of a Graph to a Canonical Form and an Algebra Arising During this Reduction `WLConv` `Nauchno-Technicheskaya Informatsiya 1968` [[pdf]](https://www.iti.zcu.cz/wl2018/pdf/wl_paper_translation.pdf)

- GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation `FiLMConv` `ICML 2020` [[pdf]](https://arxiv.org/abs/1906.12192)

- How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision `SurperGATConv` `ICLR 2021` [[pdf]](https://openreview.net/pdf?id=Wi5KUNlqWty)

- Beyond Low-Frequency Information in Graph Convolutional Networks `FAConv` `AAAI 2021` [[pdf]](https://arxiv.org/abs/2101.00797)

- Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions `EGConv` `arXiv 2021` [[pdf]](https://arxiv.org/abs/2104.01481)

- Pathfinder Discovery Networks for Neural Message Passing `PDNConv` `WWW 2021` [[pdf]](https://arxiv.org/abs/2010.12878)

- Design Space for Graph Neural Networks `GeneralConv` `NIPS 2020` [[pdf]](https://arxiv.org/pdf/2011.08843.pdf)

- Heterogeneous Graph Transformer `HGTConv` `WWW 2020` [[pdf]](https://arxiv.org/pdf/2003.01332.pdf)

- Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction `HEATConv` `arxiv 2021` [[pdf]](https://arxiv.org/abs/2106.07161)

- Heterogenous Graph Attention Network `HANConv` `WWW 2019` [[pdf]](https://arxiv.org/pdf/1903.07293.pdf)

- LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation `LGConv` `SIGIR 2020` [[pdf]](https://arxiv.org/pdf/2002.02126.pdf)

- Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud `PointGNNConv` `CVPR 2020` [[pdf]](https://arxiv.org/pdf/2003.01251.pdf)

- Recipe for a General, Powerful, Scalable Graph Transformer `GPSConv` `NIPS 2022` [[pdf]](https://arxiv.org/abs/2205.12454)

- Anti-Symmetric DGN: a stable architecture for Deep Graph Networks `AntiSymmericConv` `ICLR 2023` [[pdf]](https://arxiv.org/abs/2210.09789)

- Edge Directionality Improves Learning on Heterophilic Graphs `DirGNNConv` `LoG 2023` [[pdf]](https://arxiv.org/abs/2305.10498)

- MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing `MixHopConv` `ICML 2019` [[pdf]](https://arxiv.org/pdf/1905.00067.pdf)

- Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs `GMMConv` `CVPR 2017` [[pdf]](https://arxiv.org/pdf/1611.08402.pdf)

- Neural Message Passing for Quantum Chemistry `NNConv` `ICML 2017` [[pdf]](https://arxiv.org/pdf/1704.01212.pdf)

## Graph Pooling

常用的简单的图池化方法有 SumPooling, AvgPooling 和 MaxPooling，这里列举一些其他的更加复杂的图池化方法，参考了 [pytorch-geometric](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#pooling-layers) 和 [dgl](https://docs.dgl.ai/api/python/nn-pytorch.html#global-pooling-layers) 中的图池化函数。

- An End-to-End Deep Learning Architecture for Graph Classification `SortPooling` `AAAI 2018` [[pdf]](https://ojs.aaai.org/index.php/AAAI/article/view/11782)

- Graph U-Nets `TopKPooling` `ICML 2019` [[pdf]](https://arxiv.org/abs/1905.05178)

- Self-Attention Graph Pooling `SAGPooling` `ICML 2019` [[pdf]](https://arxiv.org/abs/1904.08082)

- Gated Graph Sequence Neural Networks `GlobalAttentionPooling` `ICLR 2016` [[pdf]](https://arxiv.org/abs/1511.05493)

- Order Matters: Sequence to sequence for sets `Set2SetPooling` `ICLR 2016` [[pdf]](https://arxiv.org/abs/1511.06391)

- Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks `SetTransformerPooling` `ICML 2019` [[pdf]](https://arxiv.org/abs/1810.00825)

- Towards Graph Pooling by Edge Contraction and Edge Contraction Pooling for Graph Neural Networks  `EdgePooling` `ICML 2019` `arxiv 2019` [[pdf]](https://mediatum.ub.tum.de/doc/1521739/document.pdf)[[pdf]](https://arxiv.org/abs/1905.10990)

- ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations `ASAPooling` `AAAI 2020` [[pdf]](https://arxiv.org/abs/1911.07979)

- Path Integral Based Convolution and Pooling for Graph Neural Networks `PANPooling` `NIPS 2020` [[pdf]](https://arxiv.org/abs/2006.16811)

- Memory-Based Graph Networks `MemPooling` `ICLR 2020` [[pdf]](https://arxiv.org/abs/2002.09518)

## GAE

- Variational Graph Auto-Encoders `arxiv` [[pdf]](https://arxiv.org/abs/1611.07308)

## AI4Science

- Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype `Nature` [[pdf]](https://www.nature.com/articles/s41587-019-0201-4)

- Open Graph Benchmark: Datasets for Machine Learning on Graphs `NIPS` [[pdf]](https://arxiv.org/abs/2005.00687)

- Benchmarking Graph Neural Networks `JMLR` [[pdf]](https://arxiv.org/abs/2003.00982)

## Survey

- A Comprehensive Survey on Graph Neural Networks `TNNLS` [[pdf]](https://ieeexplore.ieee.org/document/9046288/)

- Graph Neural Networks: A Review of Methods and Applications `AI Open` [[pdf]](https://arxiv.org/abs/1812.08434)

## Recommender System

- Neural Graph Collaborative Filtering `SIGIR 2019` [[pdf]](https://arxiv.org/abs/1905.08108)