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A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention

Bandyopadhyay, S and Aggarwal, M and Murty, MN (2021) A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention. In: 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 11-14 May 2021, pp. 554-565.

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Official URL: https://doi.org/10.1007/978-3-030-75762-5_44


Graph classification has been a classical problem of interest in machine learning and data mining because of its role in biological and social network analysis. Due to the recent success of graph neural networks for node classification and representation, researchers started extending them for the entire graph classification purpose. The main challenge is to represent the whole graph by a single vector which can be used to classify the graph in an end-to-end fashion. Global pooling, where node representations are directly aggregated to form the graph representation and more recently hierarchical pooling, where the whole graph is converted to a smaller graph through a set of hierarchies, are proposed in the literature. Though hierarchical pooling shows promising results for graph classification, it looses a significant amount of information in the hierarchical architecture. To address this, we propose a novel hybrid graph pooling architecture, which finds the importance of different hierarchies of pooling and aggregates them accordingly. We use a series of graph isomorphism networks, along with a bi-directional LSTM with self attention to implement the proposed hybrid pooling. Experiments show the merit of the proposed architecture with respect to a diverse set of state-of-the-art algorithms on multiple datasets. © 2021, Springer Nature Switzerland AG.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH
Keywords: Classification (of information); Graph theory; Long short-term memory; Network architecture, Amount of information; Classical problems; Graph classification; Graph neural networks; Graph representation; Hierarchical architectures; Proposed architectures; State-of-the-art algorithms, Data mining
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 28 Nov 2021 09:55
Last Modified: 28 Nov 2021 09:55
URI: http://eprints.iisc.ac.in/id/eprint/70007

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