Distributional Learning for Network Alignment with Global Constraints
Article No.: 88, Pages 1 - 16
Abstract
Network alignment, pairing corresponding nodes across the source and target networks, plays an important role in many data mining tasks. Extensive studies focus on learning node embeddings across different networks in a unified space. However, these methods have not taken the large structural discrepancy between aligned nodes into account and, thus, are largely confined by the deterministic representations of nodes. In this work, we propose a novel network alignment framework highlighted by distributional learning and globally optimal alignment. By modeling the uncertainty of each node by Gaussian distribution, our framework builds similarity matrices on the Wasserstein distance between distributions and applies Sinkhorn operation, which learns the globally optimal mapping in an end-to-end fashion. We show that each integrated part of the framework contributes to the overall performance. Under a variety of experimental settings, our alignment framework shows superior accuracy and efficiency to the state-of-the-art.
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Index Terms
- Distributional Learning for Network Alignment with Global Constraints
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Association for Computing Machinery
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Publication History
Published: 12 February 2024
Online AM: 20 December 2023
Accepted: 15 December 2023
Revised: 10 September 2023
Received: 25 October 2022
Published in TKDD Volume 18, Issue 4
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- National Natural Science Foundation of China
- Shanghai Pilot Program for Basic Research – Shanghai Jiao Tong University
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