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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples

February 1, 2023

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Authors

Wei Duan

University of Technology Sydney


Junyu Xuan

University of Technology Sydney


Maoying Qiao

Australian Catholic University


Jie Lu

University of Technology Sydney


DOI:

10.1609/aaai.v36i6.20608


Abstract:

Graph Convolutional Neural Networks (GCNs) have been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive samples). However, beyond these neighbouring nodes, graphs have a large, dark, all-but forgotten world in which we find the non-neighbouring nodes (negative samples). In this paper, we show that this great dark world holds a substantial amount of information that might be useful for representation learning. Most specifically, it can provide negative information about the node representations. Our overall idea is to select appropriate negative samples for each node and incorporate the negative information contained in these samples into the representation updates. Moreover, we show that the process of selecting the negative samples is not trivial. Our theme therefore begins by describing the criteria for a good negative sample, followed by a determinantal point process algorithm for efficiently obtaining such samples. A GCN, boosted by diverse negative samples, then jointly considers the positive and negative information when passing messages. Experimental evaluations show that this idea not only improves the overall performance of standard representation learning but also significantly alleviates over-smoothing problems.

Topics: AAAI

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HOW TO CITE:

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples Proceedings of the AAAI Conference on Artificial Intelligence (2022) 6550-6558.

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples AAAI 2022, 6550-6558.

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu (2022). Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. Proceedings of the AAAI Conference on Artificial Intelligence, 6550-6558.

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu. Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.6550-6558.

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu. 2022. Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. "Proceedings of the AAAI Conference on Artificial Intelligence". 6550-6558.

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu. (2022) "Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples", Proceedings of the AAAI Conference on Artificial Intelligence, p.6550-6558

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu, "Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples", AAAI, p.6550-6558, 2022.

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu. "Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.6550-6558.

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu. "Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 6550-6558.

Wei Duan||Junyu Xuan||Maoying Qiao||Jie Lu. Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. AAAI[Internet]. 2022[cited 2023]; 6550-6558.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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