Learnable Structural Semantic Readout for Graph Classification

• URL: http://arxiv.org/abs/2111.11523v1
• Date: Mon, 22 Nov 2021 20:44:27 GMT
• Title: Learnable Structural Semantic Readout for Graph Classification
• Authors: Dongha Lee, Su Kim, Seonghyeon Lee, Chanyoung Park, Hwanjo Yu
• Abstract summary: We propose structural semantic readout (SSRead) to summarize the node representations at the position-level. SSRead aims to identify structurally-meaningful positions by using the semantic alignment between its nodes and structural prototypes. Our experimental results demonstrate that SSRead significantly improves the classification performance and interpretability of GNN classifiers.
• Score: 23.78861906423389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation. However, such global aggregation does not consider the structural information of each node, which results in information loss on the global structure. Particularly, it limits the discrimination power by enforcing the same weight parameters of the classifier for all the node representations; in practice, each of them contributes to target classes differently depending on its structural semantic. In this work, we propose structural semantic readout (SSRead) to summarize the node representations at the position-level, which allows to model the position-specific weight parameters for classification as well as to effectively capture the graph semantic relevant to the global structure. Given an input graph, SSRead aims to identify structurally-meaningful positions by using the semantic alignment between its nodes and structural prototypes, which encode the prototypical features of each position. The structural prototypes are optimized to minimize the alignment cost for all training graphs, while the other GNN parameters are trained to predict the class labels. Our experimental results demonstrate that SSRead significantly improves the classification performance and interpretability of GNN classifiers while being compatible with a variety of aggregation functions, GNN architectures, and learning frameworks.

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