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ASAP: Adaptive structure aware pooling for learning hierarchical graph representations

Ranjan, E and Sanyal, S and Talukdar, P (2020) ASAP: Adaptive structure aware pooling for learning hierarchical graph representations. In: AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 7 - 12 February 2020, New York, pp. 5470-5477.

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Official URL: https://doi.org/10.1609/aaai.v34i04.5997


Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizing the information present in the nodes. Existing pooling methods either fail to effectively capture the graph substructure or do not easily scale to large graphs. In this work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse and differentiable pooling method that addresses the limitations of previous graph pooling architectures. ASAP utilizes a novel self-attention network along with a modified GNN formulation to capture the importance of each node in a given graph. It also learns a sparse soft cluster assignment for nodes at each layer to effectively pool the subgraphs to form the pooled graph. Through extensive experiments on multiple datasets and theoretical analysis, we motivate our choice of the components used in ASAP. Our experimental results show that combining existing GNN architectures with ASAP leads to state-of-the-art results on multiple graph classification benchmarks. ASAP has an average improvement of 4, compared to current sparse hierarchical state-of-the-art method. We make the source code of ASAP available to encourage reproducible research 1 Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Item Type: Conference Paper
Publication: AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
Publisher: AAAI press
Additional Information: The copyright for this article belongs to AAAI press.
Keywords: Graph theory; Network architecture; Neural networks, Adaptive structure; Cluster assignment; Graph classification; Graph neural networks; Graph structured data; Hierarchical graph representations; Hierarchical state; Reproducible research, Graph structures
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 07 Feb 2023 10:50
Last Modified: 07 Feb 2023 10:50
URI: https://eprints.iisc.ac.in/id/eprint/80016

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