Network Alignment with Holistic Embeddings (Extended Abstract)
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Duong, Chi Thang
Nguyen, Tam Thanh
Tong, Vinh Van
Sattar, Abdul
Yin, Hongzhi
Nguyen, Quoc Viet Hung
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Kuala Lumpur, Malaysia
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Abstract
Network alignment is the task of identifying topo-logically and semantically similar nodes across (two) different networks. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different types of network information or modalities. In this paper, we pro-pose a novel end-to-end alignment framework that can lever-age different modalities to compare and align network nodes in an efficient way. A comprehensive evaluation on various datasets shows that our technique outperforms state-of-the-art approaches. Our source code is available at https://github.com/thanhtrunghuynh93/holisticEmbeddingsNA.
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2022 IEEE 38th International Conference on Data Engineering (ICDE)
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© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Data engineering and data science
Science & Technology
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Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
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Thanh, TH; Thang, CD; Thanh, TN; Van, VT; Sattar, A; Yin, H; Quoc, VHN, Network Alignment with Holistic Embeddings (Extended Abstract), 2022 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022, pp. 1509-1510