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

Learning Signed Network Embedding via Graph Attention

February 1, 2023

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Authors

Yu Li

Jilin University


Yuan Tian

Jilin University


Jiawei Zhang

Florida State University


Yi Chang

Jilin University


DOI:

10.1609/aaai.v34i04.5911


Abstract:

Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets.

Topics: AAAI

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HOW TO CITE:

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang Learning Signed Network Embedding via Graph Attention Proceedings of the AAAI Conference on Artificial Intelligence (2020) 4772-4779.

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang Learning Signed Network Embedding via Graph Attention AAAI 2020, 4772-4779.

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang (2020). Learning Signed Network Embedding via Graph Attention. Proceedings of the AAAI Conference on Artificial Intelligence, 4772-4779.

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang. Learning Signed Network Embedding via Graph Attention. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.4772-4779.

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang. 2020. Learning Signed Network Embedding via Graph Attention. "Proceedings of the AAAI Conference on Artificial Intelligence". 4772-4779.

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang. (2020) "Learning Signed Network Embedding via Graph Attention", Proceedings of the AAAI Conference on Artificial Intelligence, p.4772-4779

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang, "Learning Signed Network Embedding via Graph Attention", AAAI, p.4772-4779, 2020.

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang. "Learning Signed Network Embedding via Graph Attention". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.4772-4779.

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang. "Learning Signed Network Embedding via Graph Attention". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 4772-4779.

Yu Li||Yuan Tian||Jiawei Zhang||Yi Chang. Learning Signed Network Embedding via Graph Attention. AAAI[Internet]. 2020[cited 2023]; 4772-4779.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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