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)
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244092.pdf - Accepted Version 1MB |
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 |
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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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