NIP-GCN: An Augmented Graph Convolutional Network with Node Interaction Patterns

Chandra, M., Ganguly, D. , Mitra, P., Pal, B. and Thomas, J. (2021) NIP-GCN: An Augmented Graph Convolutional Network with Node Interaction Patterns. In: 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 21), 11-15 July 2021, pp. 2242-2246. (doi: 10.1145/3404835.3463082)

Abstract

In this paper, we propose an augmented Graph Convolutional Network (GCN) mechanism wherein additional information of local interaction patterns between a node with its neighbors (specifically, in the form of distribution of cosine similarity values of a pre-trained node vector with its neighbors) is used to enrich a node's representation prior to training a GCN. This provides additional information about the structural properties of a node, which the standard convolution operation in a GCN can then leverage for obtaining potentially improved effectiveness in a down-stream task. Our experiments demonstrate that adding these node interaction patterns (NIPs) along with an additional noise-contrastive pairwise document similarity objective within a GCN improves the linked document classification task.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ganguly, Dr Debasis
• Authors: Chandra, M., Ganguly, D., Mitra, P., Pal, B., and Thomas, J.
• College/School: College of Science and Engineering > School of Computing Science
• Research Group: IDA, School of Computing
• Copyright Holders: Copyright © 2021 Association for Computing Machinery
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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