research-article

Feature Fusion Based Subgraph Classification for Link Prediction

Authors:

Meng WangAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 985 - 994

https://doi.org/10.1145/3340531.3411966

Published: 19 October 2020 Publication History

Abstract

Link prediction, which centers on whether or not a pair of nodes is likely to be connected, is a fundamental problem in complex network analysis. Network-embedding-based link prediction has shown strong performance and robustness in previous studies on complex networks, recommendation systems, and knowledge graphs. This approach has certain drawbacks, however; namely, the hierarchical structure of a subgraph is ignored and the importance of different nodes is not distinguished. In this study, we established the Subgraph Hierarchy Feature Fusion (SHFF) model for link prediction. To probe the existence of links between node pairs, the SHFF first extracts a subgraph around the two nodes and learns a function to map the subgraph to a vector for subsequent classification. This reveals any link between the two target nodes. The SHFF learns a function to obtain a representation of the extracted subgraph by hierarchically aggregating the features of nodes in that subgraph, which is accomplished by grouping nodes with similar structures and assigning different importance to the nodes during the feature fusion process. We compared the proposed model against other state-of-the-art link-prediction methods on a wide range of data sets to find that it consistently outperforms them.

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Cited By

Fang SLi LBai SMa ZChen X(2024)Link prediction based on fundamental heuristic elementsInternational Journal of Modern Physics C10.1142/S0129183124501614Online publication date: 21-Jun-2024
https://doi.org/10.1142/S0129183124501614
Fang ZTan SWang Y(2024)A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign PredictionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.328092435:10(14659-14670)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3280924
Dong HLi LTian DSun YZhao Y(2024)Dynamic link prediction by learning the representation of node-pair via graph neural networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122685241:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122685
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Index Terms

Feature Fusion Based Subgraph Classification for Link Prediction
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks
2. Networks
  1. Network architectures

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

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Published: 19 October 2020

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National Natural Science Foundation of China
National Key Research and Development Program of China
Natural Science Foundation of Jiangsu Province

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CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

Virtual Event, Ireland

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Cited By

Fang SLi LBai SMa ZChen X(2024)Link prediction based on fundamental heuristic elementsInternational Journal of Modern Physics C10.1142/S0129183124501614Online publication date: 21-Jun-2024
https://doi.org/10.1142/S0129183124501614
Fang ZTan SWang Y(2024)A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign PredictionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.328092435:10(14659-14670)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3280924
Dong HLi LTian DSun YZhao Y(2024)Dynamic link prediction by learning the representation of node-pair via graph neural networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122685241:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122685
Zhou MWang FChen ZWu JLiu GLiao H(2024)Weak link prediction based on hyper latent distance in complex networkExpert Systems with Applications10.1016/j.eswa.2023.121843238(121843)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121843
Sari GPao HPribadi RIqbal M(2024)Multi-feature Subgraph Fusion with Text Knowledge on Citation Link PredictionApplied and Computational Mathematics10.1007/978-981-97-2136-8_22(299-308)Online publication date: 25-Jul-2024
https://doi.org/10.1007/978-981-97-2136-8_22
Liu CYang YXie ZLu HDing YChua TLauw HSi LTerzi ETsaparas P(2023)Position-Aware Subgraph Neural Networks with Data-Efficient LearningProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570429(643-651)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570429
Li PPan LLi KPlant CBöhm C(2023)Interpretable Subgraph Feature Extraction for Hyperlink Prediction2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00037(279-288)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00037
Li LWen YBai SLiu P(2022)Link prediction in weighted networks via motif predictorKnowledge-Based Systems10.1016/j.knosys.2022.108402242:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108402
Lai DLiu ZHuang JChong ZWu WNardini CDemartini GZuccon GCulpepper JHuang ZTong H(2021)Attention Based Subgraph Classification for Link Prediction by Network Re-weightingProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482060(3171-3175)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482060

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