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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Independence Promoted Graph Disentangled Networks

February 1, 2023

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Authors

Yanbei Liu

Tiangong University


Xiao Wang

Beijing University of Posts and Telecommunications


Shu Wu

Chinese Academy of Sciences


Zhitao Xiao

Tiangong University


DOI:

10.1609/aaai.v34i04.5929


Abstract:

We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node representation by describing its neighborhood as a perceptual whole in a holistic manner while ignoring the entanglement of the latent factors. However, a real-world graph is formed by the complex interaction of many latent factors (e.g., the same hobby, education or work in social network). While little effort has been made toward exploring the disentangled representation in GCNs. In this paper, we propose a novel Independence Promoted Graph Disentangled Networks (IPGDN) to learn disentangled node representation while enhancing the independence among node representations. In particular, we firstly present disentangled representation learning by neighborhood routing mechanism, and then employ the Hilbert-Schmidt Independence Criterion (HSIC) to enforce independence between the latent representations, which is effectively integrated into a graph convolutional framework as a regularizer at the output layer. Experimental studies on real-world graphs validate our model and demonstrate that our algorithms outperform the state-of-the-arts by a wide margin in different network applications, including semi-supervised graph classification, graph clustering and graph visualization.

Topics: AAAI

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HOW TO CITE:

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao Independence Promoted Graph Disentangled Networks Proceedings of the AAAI Conference on Artificial Intelligence (2020) 4916-4923.

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao Independence Promoted Graph Disentangled Networks AAAI 2020, 4916-4923.

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao (2020). Independence Promoted Graph Disentangled Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 4916-4923.

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao. Independence Promoted Graph Disentangled Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.4916-4923.

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao. 2020. Independence Promoted Graph Disentangled Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 4916-4923.

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao. (2020) "Independence Promoted Graph Disentangled Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.4916-4923

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao, "Independence Promoted Graph Disentangled Networks", AAAI, p.4916-4923, 2020.

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao. "Independence Promoted Graph Disentangled Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.4916-4923.

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao. "Independence Promoted Graph Disentangled Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 4916-4923.

Yanbei Liu||Xiao Wang||Shu Wu||Zhitao Xiao. Independence Promoted Graph Disentangled Networks. AAAI[Internet]. 2020[cited 2023]; 4916-4923.


ISSN: 2374-3468


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Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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