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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 8: AAAI-22 Technical Tracks 8

SAIL: Self-Augmented Graph Contrastive Learning

February 1, 2023

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Authors

Lu Yu

King Abdullah University of Science and Technology, Saudi Arabia Ant Group, Hangzhou, China


Shichao Pei

King Abdullah University of Science and Technology, Saudi Arabia


Lizhong Ding

Inception Institute of Artificial Intelligence, UAE


Jun Zhou

Ant Group, Hangzhou, China


Longfei Li

Ant Group, Hangzhou, China


Chuxu Zhang

Brandeis University, USA


Xiangliang Zhang

University of Notre Dame, USA King Abdullah University of Science and Technology, Saudi Arabia


DOI:

10.1609/aaai.v36i8.20875


Abstract:

This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirical demonstration about the non-steady performance of GNNs over different graph datasets, when the supervision signals are not appropriately defined. The performance of GNNs depends on both the node feature smoothness and the locality of graph structure. To smooth the discrepancy of node proximity measured by graph topology and node feature, we proposed SAIL - a novel self-augmented graph contrastive learning framework, with two complementary self-distilling regularization modules, i.e., intra- and inter-graph knowledge distillation. We demonstrate the competitive performance of SAIL on a variety of graph applications. Even with a single GNN layer, SAIL has consistently competitive or even better performance on various benchmark datasets, comparing with state-of-the-art baselines.

Topics: AAAI

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HOW TO CITE:

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang SAIL: Self-Augmented Graph Contrastive Learning Proceedings of the AAAI Conference on Artificial Intelligence (2022) 8927-8935.

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang SAIL: Self-Augmented Graph Contrastive Learning AAAI 2022, 8927-8935.

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang (2022). SAIL: Self-Augmented Graph Contrastive Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 8927-8935.

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang. SAIL: Self-Augmented Graph Contrastive Learning. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.8927-8935.

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang. 2022. SAIL: Self-Augmented Graph Contrastive Learning. "Proceedings of the AAAI Conference on Artificial Intelligence". 8927-8935.

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang. (2022) "SAIL: Self-Augmented Graph Contrastive Learning", Proceedings of the AAAI Conference on Artificial Intelligence, p.8927-8935

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang, "SAIL: Self-Augmented Graph Contrastive Learning", AAAI, p.8927-8935, 2022.

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang. "SAIL: Self-Augmented Graph Contrastive Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.8927-8935.

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang. "SAIL: Self-Augmented Graph Contrastive Learning". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 8927-8935.

Lu Yu||Shichao Pei||Lizhong Ding||Jun Zhou||Longfei Li||Chuxu Zhang||Xiangliang Zhang. SAIL: Self-Augmented Graph Contrastive Learning. AAAI[Internet]. 2022[cited 2023]; 8927-8935.


ISSN: 2374-3468


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