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Authors: Yuexin Wu ; Hanxiao Liu and Yiming Yang

Affiliation: Carnegie Mellon University, 5000 Forbes Ave, 15213, Pittsburgh, PA and U.S.A.

ISBN: 978-989-758-330-8

Keyword(s): Matrix Completion, Graph Convolution, Deep Learning.

Abstract: Leveraging intrinsic graph structures in data to improve bipartite edge prediction has become an increasingly important topic in the recent machine learning area. Existing methods, however, are facing open challenges in how to enrich model expressiveness and reduce computational complexity for scalability. This paper addresses both challenges with a novel approach that uses a multi-layer/hop neural network to model a hidden space, and the first-order Chebyshev approximation to reduce training time complexity. Our experiments on benchmark datasets for collaborative filtering, citation network analysis, course prerequisite prediction and drug-target interaction prediction show the advantageous performance of the proposed approach over several state-of-the-art methods.

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Paper citation in several formats:
Wu Y., Liu H. and Yang Y. (2018). Graph Convolutional Matrix Completion for Bipartite Edge Prediction.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 51-60. DOI: 10.5220/0006900000510060

@conference{kdir18,
author={Yuexin Wu and Hanxiao Liu and Yiming Yang},
title={Graph Convolutional Matrix Completion for Bipartite Edge Prediction},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2018},
pages={51-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006900000510060},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Graph Convolutional Matrix Completion for Bipartite Edge Prediction
SN - 978-989-758-330-8
AU - Wu Y.
AU - Liu H.
AU - Yang Y.
PY - 2018
SP - 51
EP - 60
DO - 10.5220/0006900000510060

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