CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph ClassificationGraph Representation LearningRepresentation LearningSimilar Papers 제목 키워드 기반
Towards Interpretable Molecular Graph Representation Learning
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative importance of interactions between molecular…
Drug DiscoveryGraph Representation LearningProperty PredictionRepresentation LearningTowards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative importance of interactions between molecular…
Drug DesignDrug DiscoveryGraph Representation Learningmolecular representation+3Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
Graph neural networks (GNNs) have led to major breakthroughs in a variety of domains such as drug discovery, social network analysis, and travel time estimation. However, they lack interpretability which hinders human tr…
Drug DiscoveryTravel Time EstimationHierarchical Pooling and Explainability in Graph Neural Networks for Tumor and Tissue-of-Origin Classification Using RNA-seq Data
This study explores the use of graph neural networks (GNNs) with hierarchical pooling and multiple convolution layers for cancer classification based on RNA-seq data. We combine gene expression data from The Cancer Genom…
Dimensionality ReductionCancer ClassificationAttentive pooling for Group Activity Recognition
In group activity recognition, hierarchical framework is widely adopted to represent the relationships between individuals and their corresponding group, and has achieved promising performance. However, the existing meth…
Activity RecognitionGroup Activity Recognition