【导读】ICLR2020论文收到2594篇论文提交,有687篇被接受,接受率为26.5%。在关于图机器学习方面,Sergei Ivanov整理了关于图机器学习方面的高分论文,有49篇关于图机器学习论文,专知进一步整理了论文阅读欢迎查看!
总共有152篇论文在题目中包含了一个“Graph”,其中有49篇论文被接受。所有图表论文的平均评分是4.5,而被接受的论文的平均评分是6.3。
1 图神经网络的逻辑表达性 The Logical Expressiveness of Graph Neural Networks
Pablo Barceló, Egor V. Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, Juan Pablo Silva
代码地址:
https://anonymous.4open.science/r/787222e2-ad5e-4810-a788-e80f0fe7eff0/
论文地址:
https://openreview.net/forum?id=r1lZ7AEKvB
本文重点研究了图神经网络的表达特性。审稿人对作者充分且一致地回答了他们的问题表示满意,认为这是一篇应该被接受的强有力的论文
2 Hyper-SAGNN:一种基于自注意力的超图神经网络 Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
Ruochi Zhang, Yuesong Zou, Jian Ma
代码地址:
https://drive.google.com/drive/folders/1kIOc4SlAJllUJsrr2OnZ4izIQIw2JexU?usp=sharing
论文地址:
https://openreview.net/forum?id=ryeHuJBtPH
本文介绍了一种新的神经网络模型,该模型可以表示可变尺寸的超边缘,并通过实验证明了该模型在一些问题上可以改进或匹配目前的技术水平。
3. 论节点嵌入与结构图表示的等价性,On the Equivalence between Node Embeddings and Structural Graph Representations
Balasubramaniam Srinivasan, Bruno Ribeiro
论文地址:
https://openreview.net/forum?id=SJxzFySKwH
本文给出了节点嵌入与结构图表示之间的关系。通过对结构节点表示的含义和节点嵌入的含义的仔细定义,利用置换群,作者在定理2中证明了节点嵌入不能表示结构表示中没有的任何额外信息。然后,本文对三个任务进行了实证实验,并在第四个任务中对理论结果进行了说明。
4. LambdaNet:使用图神经网络的概率类型推断,LambdaNet: Probabilistic Type Inference using Graph Neural Networks
Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig
论文地址:
https://openreview.net/forum?id=Hkx6hANtwH
本文提出了一种基于图神经网络的动态语言类型推理方法。Reviewer(以及区域主席)喜欢GNNs在实际问题、演示和结果中的这种新颖而有用的应用。明确的接受。
5. 定向消息传递分子图,Directional Message Passing for Molecular Graphs
Johannes Klicpera, Janek Groß, Stephan Günnemann
论文地址:
https://openreview.net/forum?id=B1eWbxStPH
本文研究了量子化学的图神经网络,在此基础上加入了一些物理方面的创新。特别地,它在保持等方差的同时考虑方向边缘信息。
49篇完整「图机器学习GML」论文列表:
论文题目 | 论文地址 | 接受类型 | 打分 |
The Logical Expressiveness of Graph Neural Net... | https://openreview.net/forum?id=r1lZ7AEKvB | Accept (Spotlight) | 8 |
Hyper-SAGNN: a self-attention based graph neur... | https://openreview.net/forum?id=ryeHuJBtPH | Accept (Poster) | 8 |
On the Equivalence between Node Embeddings and... | https://openreview.net/forum?id=SJxzFySKwH | Accept (Poster) | 7.33 |
LambdaNet: Probabilistic Type Inference using ... | https://openreview.net/forum?id=Hkx6hANtwH | Accept (Poster) | 7.33 |
Directional Message Passing for Molecular Graphs | https://openreview.net/forum?id=B1eWbxStPH | Accept (Spotlight) | 7.33 |
What graph neural networks cannot learn: depth... | https://openreview.net/forum?id=B1l2bp4YwS | Accept (Poster) | 7.33 |
Graph Neural Networks Exponentially Lose Expre... | https://openreview.net/forum?id=S1ldO2EFPr | Accept (Spotlight) | 7.33 |
Physics-aware Difference Graph Networks for Sp... | https://openreview.net/forum?id=r1gelyrtwH | Accept (Poster) | 7.33 |
GLAD: Learning Sparse Graph Recovery | https://openreview.net/forum?id=BkxpMTEtPB | Accept (Poster) | 7.33 |
GraphZoom: A Multi-level Spectral Approach for... | https://openreview.net/forum?id=r1lGO0EKDH | Accept (Talk) | 7.33 |
DeepSphere: a graph-based spherical CNN | https://openreview.net/forum?id=B1e3OlStPB | Accept (Spotlight) | 6.67 |
Query2box: Reasoning over Knowledge Graphs in ... | https://openreview.net/forum?id=BJgr4kSFDS | Accept (Poster) | 6.67 |
Dynamically Pruned Message Passing Networks fo... | https://openreview.net/forum?id=rkeuAhVKvB | Accept (Poster) | 6.67 |
Inductive Matrix Completion Based on Graph Neu... | https://openreview.net/forum?id=ByxxgCEYDS | Accept (Spotlight) | 6.67 |
On the geometry and learning low-dimensional e... | https://openreview.net/forum?id=SkxQp1StDH | Accept (Poster) | 6.67 |
Reinforced Genetic Algorithm Learning for Opti... | https://openreview.net/forum?id=rkxDoJBYPB | Accept (Poster) | 6.67 |
Reinforcement Learning Based Graph-to-Sequence... | https://openreview.net/forum?id=HygnDhEtvr | Accept (Poster) | 6.67 |
Learning to Retrieve Reasoning Paths over Wiki... | https://openreview.net/forum?id=SJgVHkrYDH | Accept (Poster) | 6.67 |
A Fair Comparison of Graph Neural Networks for... | https://openreview.net/forum?id=HygDF6NFPB | Accept (Poster) | 6.67 |
You CAN Teach an Old Dog New Tricks! On Traini... | https://openreview.net/forum?id=BkxSmlBFvr | Accept (Poster) | 6.67 |
Geom-GCN: Geometric Graph Convolutional Networks | https://openreview.net/forum?id=S1e2agrFvS | Accept (Spotlight) | 6.67 |
HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DET... | https://openreview.net/forum?id=SJeqs6EFvB | Accept (Spotlight) | 6.67 |
Inductive representation learning on temporal ... | https://openreview.net/forum?id=rJeW1yHYwH | Accept (Poster) | 6.67 |
Measuring and Improving the Use of Graph Infor... | https://openreview.net/forum?id=rkeIIkHKvS | Accept (Poster) | 6.33 |
Memory-Based Graph Networks | https://openreview.net/forum?id=r1laNeBYPB | Accept (Poster) | 6 |
Adaptive Structural Fingerprints for Graph Att... | https://openreview.net/forum?id=BJxWx0NYPr | Accept (Poster) | 6 |
Graph Constrained Reinforcement Learning for N... | https://openreview.net/forum?id=B1x6w0EtwH | Accept (Poster) | 6 |
Graph Convolutional Reinforcement Learning | https://openreview.net/forum?id=HkxdQkSYDB | Accept (Poster) | 6 |
Inductive and Unsupervised Representation Lear... | https://openreview.net/forum?id=rkem91rtDB | Accept (Poster) | 6 |
StructPool: Structured Graph Pooling via Condi... | https://openreview.net/forum?id=BJxg_hVtwH | Accept (Poster) | 6 |
GraphSAINT: Graph Sampling Based Inductive Lea... | https://openreview.net/forum?id=BJe8pkHFwS | Accept (Poster) | 6 |
Physics-as-Inverse-Graphics: Unsupervised Phys... | https://openreview.net/forum?id=BJeKwTNFvB | Accept (Poster) | 6 |
Strategies for Pre-training Graph Neural Networks | https://openreview.net/forum?id=HJlWWJSFDH | Accept (Spotlight) | 6 |
Deep Graph Matching Consensus | https://openreview.net/forum?id=HyeJf1HKvS | Accept (Poster) | 6 |
InfoGraph: Unsupervised and Semi-supervised Gr... | https://openreview.net/forum?id=r1lfF2NYvH | Accept (Spotlight) | 6 |
Pruned Graph Scattering Transforms | https://openreview.net/forum?id=rJeg7TEYwB | Accept (Poster) | 6 |
Curvature Graph Network | https://openreview.net/forum?id=BylEqnVFDB | Accept (Poster) | 6 |
Composition-based Multi-Relational Graph Convo... | https://openreview.net/forum?id=BylA_C4tPr | Accept (Poster) | 6 |
GraphAF: a Flow-based Autoregressive Model for... | https://openreview.net/forum?id=S1esMkHYPr | Accept (Poster) | 6 |
Graph inference learning for semi-supervised c... | https://openreview.net/forum?id=r1evOhEKvH | Accept (Poster) | 6 |
Probability Calibration for Knowledge Graph Em... | https://openreview.net/forum?id=S1g8K1BFwS | Accept (Poster) | 5.75 |
Neural Execution of Graph Algorithms | https://openreview.net/forum?id=SkgKO0EtvS | Accept (Poster) | 5.67 |
Visual Representation Learning with 3D View-Co... | https://openreview.net/forum?id=BJxt60VtPr | Accept (Poster) | 5.25 |
Abstract Diagrammatic Reasoning with Multiplex... | https://openreview.net/forum?id=ByxQB1BKwH | Accept (Poster) | 5 |
FEW-SHOT LEARNING ON GRAPHS VIA SUPER-CLASSES ... | https://openreview.net/forum?id=Bkeeca4Kvr | Accept (Poster) | 5 |
Differentiable learning of numerical rules in ... | https://openreview.net/forum?id=rJleKgrKwS | Accept (Poster) | 5 |
Learning deep graph matching with channel-inde... | https://openreview.net/forum?id=rJgBd2NYPH | Accept (Poster) | 5 |
DropEdge: Towards Deep Graph Convolutional Net... | https://openreview.net/forum?id=Hkx1qkrKPr | Accept (Poster) | 4 |
Efficient Probabilistic Logic Reasoning with G... | https://openreview.net/forum?id=rJg76kStwH | Accept (Poster) | 2.33 |
参考地址:
https://medium.com/@sergei.ivanov_24894/iclr-2020-graph-papers-9bc2e90e56b0
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