CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning

• URL: http://arxiv.org/abs/2012.05980v1
• Date: Thu, 10 Dec 2020 21:14:18 GMT
• Title: CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
• Authors: Haoteng Tang, Guixiang Ma, Lifang He, Heng Huang, Liang Zhan
• Abstract summary: We propose a new interpretable graph pooling framework - CommPOOL. It can capture and preserve the hierarchical community structure of graphs in the graph representation learning process. CommPOOL is a general and flexible framework for hierarchical graph representation learning.
• Score: 74.90535111881358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classification. However, current HGPNNs do not take full advantage of the graph's intrinsic structures (e.g., community structure). Moreover, the pooling operations in existing HGPNNs are difficult to be interpreted. In this paper, we propose a new interpretable graph pooling framework - CommPOOL, that can capture and preserve the hierarchical community structure of graphs in the graph representation learning process. Specifically, the proposed community pooling mechanism in CommPOOL utilizes an unsupervised approach for capturing the inherent community structure of graphs in an interpretable manner. CommPOOL is a general and flexible framework for hierarchical graph representation learning that can further facilitate various graph-level tasks. Evaluations on five public benchmark datasets and one synthetic dataset demonstrate the superior performance of CommPOOL in graph representation learning for graph classification compared to the state-of-the-art baseline methods, and its effectiveness in capturing and preserving the community structure of graphs.

Related papers

• SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling [25.555741218526464]
  Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. We propose a concatenation-based graph convolution mechanism that injectively updates node representations. We also design a novel graph pooling module, called WL-SortPool, to learn important subgraph patterns in a deep-learning manner.
  arXiv  Detail & Related papers  (2024-04-21T13:11:59Z)
• Higher-order Clustering and Pooling for Graph Neural Networks [77.47617360812023]
  Graph Neural Networks achieve state-of-the-art performance on a plethora of graph classification tasks. HoscPool is a clustering-based graph pooling operator that captures higher-order information hierarchically. We evaluate HoscPool on graph classification tasks and its clustering component on graphs with ground-truth community structure.
  arXiv  Detail & Related papers  (2022-09-02T09:17:10Z)
• Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities [128.55790219377315]
  Graph neural networks have emerged as a leading architecture for many graph-level tasks. graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph.
  arXiv  Detail & Related papers  (2022-04-15T04:02:06Z)
• Edge but not Least: Cross-View Graph Pooling [76.71497833616024]
  This paper presents a cross-view graph pooling (Co-Pooling) method to better exploit crucial graph structure information. Through cross-view interaction, edge-view pooling and node-view pooling seamlessly reinforce each other to learn more informative graph-level representations.
  arXiv  Detail & Related papers  (2021-09-24T08:01:23Z)
• Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning [18.423192209359158]
  Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks. This paper proposes a hierarchical graph-level representation learning framework, which is adaptively sensitive to graph structures.
  arXiv  Detail & Related papers  (2021-04-13T06:22:24Z)
• Multilevel Graph Matching Networks for Deep Graph Similarity Learning [79.3213351477689]
  We propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects. To compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks.
  arXiv  Detail & Related papers  (2020-07-08T19:48:19Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)
• Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization [8.14036521415919]
  We present an unsupervised graph representation learning method, Unsupervised Hierarchical Graph Representation (UHGR) Our method focuses on maximizing mutual information between "local" and high-level "global" representations. The results show that the proposed method achieves comparable results to state-of-the-art supervised methods on several benchmarks.
  arXiv  Detail & Related papers  (2020-03-18T18:21:48Z)
• Deep Graph Mapper: Seeing Graphs through the Neural Lens [4.401427499962144]
  We merge Mapper with the expressive power of Graph Neural Networks (GNNs) to produce hierarchical, topologically-grounded visualisations of graphs. These visualisations do not only help discern the structure of complex graphs but also provide a means of understanding the models applied to them for solving various tasks.
  arXiv  Detail & Related papers  (2020-02-10T15:29:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.