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Multi-order Matched Neighborhood Consistent Graph Alignment in a Union Vector Space

Published: 18 July 2023 Publication History

Abstract

In this paper, we study the unsupervised plain graph alignment problem, which aims to find node correspondences across two graphs without any side information. The majority of previous works addressed UPGA based on structural information, which will inevitably lead to subgraph isomorphism issues. That is, unaligned nodes could take similar local structural information. To mitigate this issue, we present the Multi-order Matched Neighborhood Consistent (MMNC) which tries to match nodes by aligning the learned node embeddings with only a small number of pseudo alignment seeds. In particular, we extend matched neighborhood consistency (MNC) to vector space and further develop embedding-based MNC (EMNC). By minimizing the EMNC-based loss function, we can utilize the limited pseudo alignment seeds to approximate the orthogonal transformation matrix between two groups of node embeddings with high efficiency and accuracy. Through extensive experiments on public benchmarks, we show that the proposed methods achieve a good balance between alignment accuracy and speed over multiple datasets compared with existing methods.

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  • (2025)Multi-level social network alignment via adversarial learning and graphlet modelingNeural Networks10.1016/j.neunet.2025.107230185(107230)Online publication date: May-2025
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    cover image ACM Conferences
    SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2023
    3567 pages
    ISBN:9781450394086
    DOI:10.1145/3539618
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 18 July 2023

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    Author Tags

    1. deepwalk
    2. node embedding
    3. plain graph alignment

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    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    View all
    • (2025)Multi-level social network alignment via adversarial learning and graphlet modelingNeural Networks10.1016/j.neunet.2025.107230185(107230)Online publication date: May-2025
    • (2024)GABoost: Graph Alignment Boosting via Local Optimum EscapeProceedings of the ACM on Management of Data10.1145/36771352:4(1-26)Online publication date: 30-Sep-2024
    • (2024)Multi-modal Entity Alignment via Position-enhanced Multi-label PropagationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658085(366-375)Online publication date: 30-May-2024
    • (2024)Collaborative Cross-Network Embedding Framework for Network AlignmentIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.335547911:3(2989-3001)Online publication date: May-2024
    • (2024)Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00274(3559-3572)Online publication date: 13-May-2024

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