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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 10: AAAI-21 Technical Tracks 10

Class-Attentive Diffusion Network for Semi-Supervised Classification

February 1, 2023

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Authors

Jongin Lim

Seoul National University


Daeho Um

Seoul National University


Hyung Jin Chang

University of Birmingham


Dae Ung Jo

Seoul National University


Jin Young Choi

Seoul National University


DOI:

10.1609/aaai.v35i10.17043


Abstract:

Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. To this end, we first propose a novel stochastic process, called Class-Attentive Diffusion (CAD), that strengthens attention to intra-class nodes and attenuates attention to inter-class nodes. In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier. Then, we further propose an adaptive update scheme that leverages different reflection ratios of the diffusion result for each node depending on the local class-context. As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method and our CAD-Net significantly outperforms the state-of-the-art methods. Code is available at https://github.com/ljin0429/CAD-Net.

Topics: AAAI

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HOW TO CITE:

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi Class-Attentive Diffusion Network for Semi-Supervised Classification Proceedings of the AAAI Conference on Artificial Intelligence (2021) 8601-8609.

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi Class-Attentive Diffusion Network for Semi-Supervised Classification AAAI 2021, 8601-8609.

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi (2021). Class-Attentive Diffusion Network for Semi-Supervised Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 8601-8609.

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi. Class-Attentive Diffusion Network for Semi-Supervised Classification. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.8601-8609.

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi. 2021. Class-Attentive Diffusion Network for Semi-Supervised Classification. "Proceedings of the AAAI Conference on Artificial Intelligence". 8601-8609.

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi. (2021) "Class-Attentive Diffusion Network for Semi-Supervised Classification", Proceedings of the AAAI Conference on Artificial Intelligence, p.8601-8609

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi, "Class-Attentive Diffusion Network for Semi-Supervised Classification", AAAI, p.8601-8609, 2021.

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi. "Class-Attentive Diffusion Network for Semi-Supervised Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.8601-8609.

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi. "Class-Attentive Diffusion Network for Semi-Supervised Classification". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 8601-8609.

Jongin Lim||Daeho Um||Hyung Jin Chang||Dae Ung Jo||Jin Young Choi. Class-Attentive Diffusion Network for Semi-Supervised Classification. AAAI[Internet]. 2021[cited 2023]; 8601-8609.


ISSN: 2374-3468


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