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Universal Domain Adaptive Network Embedding for Node Classification

Published: 27 October 2023 Publication History

Abstract

Cross-network node classification aims to leverage the abundant knowledge from a labeled source network to help classify the node in an unlabeled target network. However, existing methods assume that label sets are identical across domains, which is easily violated in practice. Hence, we attempt to integrate network embedding with universal domain adaptation, which transfers valuable knowledge across domains without assumption on the label sets, to assist in node classification. Nonetheless, the complex network relationships between nodes increase the difficulty of this universal domain adaptive node classification task. In this work, we propose a novel Universal Domain Adaptive Network Embedding (UDANE) framework, which learns transferable node representations across networks to succeed in such a task. Technically, we first adopt the cross-network node embedding component to model comprehensive node information of both networks. Then we employ the inter-domain adaptive alignment component to exploit and relate knowledge across domains, learning domain-invariant representation for knowledge transfer. In addition, the intra-domain contrastive alignment component is proposed to learn discriminative representations beneficial for classification by sufficiently utilizing unlabeled data in the target domain. Extensive experiments have been conducted on real-world datasets, demonstrating that the proposed UDANE model outperforms the state-of-the-art baselines by a large margin.

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Cited By

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  • (2024)GALA: Graph Diffusion-Based Alignment With Jigsaw for Source-Free Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341637246:12(9038-9051)Online publication date: Dec-2024
  • (2024)ROSE: Relational and Prototypical Structure Learning for Universal Domain Adaptive HashingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.344431919(7690-7704)Online publication date: 15-Aug-2024

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Published In

cover image ACM Conferences
MM '23: Proceedings of the 31st ACM International Conference on Multimedia
October 2023
9913 pages
ISBN:9798400701085
DOI:10.1145/3581783
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Publication History

Published: 27 October 2023

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Author Tags

  1. contrastive learning
  2. cross-network node classification
  3. network embedding
  4. universal domain adaptation

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MM '23
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MM '23: The 31st ACM International Conference on Multimedia
October 29 - November 3, 2023
Ottawa ON, Canada

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View all
  • (2024)GALA: Graph Diffusion-Based Alignment With Jigsaw for Source-Free Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341637246:12(9038-9051)Online publication date: Dec-2024
  • (2024)ROSE: Relational and Prototypical Structure Learning for Universal Domain Adaptive HashingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.344431919(7690-7704)Online publication date: 15-Aug-2024

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