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Direction-Aware User Recommendation Based on Asymmetric Network Embedding

Authors:

Jiajun BuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2

Article No.: 29, Pages 1 - 23

https://doi.org/10.1145/3466754

Published: 16 November 2021 Publication History

Abstract

User recommendation aims at recommending users with potential interests in the social network. Previous works have mainly focused on the undirected social networks with symmetric relationship such as friendship, whereas recent advances have been made on the asymmetric relationship such as the following and followed by relationship. Among the few existing direction-aware user recommendation methods, the random walk strategy has been widely adopted to extract the asymmetric proximity between users. However, according to our analysis on real-world directed social networks, we argue that the asymmetric proximity captured by existing random walk based methods are insufficient due to the inbalance in-degree and out-degree of nodes.

To tackle this challenge, we propose InfoWalk, a novel informative walk strategy to efficiently capture the asymmetric proximity solely based on random walks. By transferring the direction information into the weights of each step, InfoWalk is able to overcome the limitation of edges while simultaneously maintain both the direction and proximity. Based on the asymmetric proximity captured by InfoWalk, we further propose the qualitative (DNE-L) and quantitative (DNE-T) directed network embedding methods, capable of preserving the two properties in the embedding space. Extensive experiments conducted on six real-world benchmark datasets demonstrate the superiority of the proposed DNE model over several state-of-the-art approaches in various tasks.

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Cited By

Reyhani Hamedani MRyu JKim S(2024)Random-Walk-Based or Similarity-Based Methods, Which is Better for Directed Graph Embedding?2024 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp60711.2024.00022(83-89)Online publication date: 18-Feb-2024
https://doi.org/10.1109/BigComp60711.2024.00022
Aras MGuvensan M(2023)A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket ActivitiesApplied Sciences10.3390/app13241312113:24(13121)Online publication date: 9-Dec-2023
https://doi.org/10.3390/app132413121
Rehyani Hamedani MRyu JKim SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)ELTRA: An Embedding Method based on Learning-to-Rank to Preserve Asymmetric Information in Directed GraphsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614862(2116-2125)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614862
Show More Cited By

Index Terms

Direction-Aware User Recommendation Based on Asymmetric Network Embedding
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
2. Networks
  1. Network properties
    1. Network structure
      1. Topology analysis and generation

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 2

April 2022

587 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3484931

Issue’s Table of Contents

Copyright © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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Publication History

Published: 16 November 2021

Accepted: 01 May 2021

Revised: 01 May 2021

Received: 01 November 2020

Published in TOIS Volume 40, Issue 2

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National Key Research and Development Program
National Natural Science Foundation of China
NSERC Discovery
Alibaba-Zhejiang University Joint Institute of Frontier Technologies

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Cited By

Reyhani Hamedani MRyu JKim S(2024)Random-Walk-Based or Similarity-Based Methods, Which is Better for Directed Graph Embedding?2024 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp60711.2024.00022(83-89)Online publication date: 18-Feb-2024
https://doi.org/10.1109/BigComp60711.2024.00022
Aras MGuvensan M(2023)A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket ActivitiesApplied Sciences10.3390/app13241312113:24(13121)Online publication date: 9-Dec-2023
https://doi.org/10.3390/app132413121
Rehyani Hamedani MRyu JKim SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)ELTRA: An Embedding Method based on Learning-to-Rank to Preserve Asymmetric Information in Directed GraphsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614862(2116-2125)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614862
Zhu YSwami ASegarra S(2023)Free Energy Node Embedding via Generalized Skip-Gram With Negative SamplingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.320617535:8(8024-8036)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3206175
Wu HLong JLi NYu DNg M(2022)Adversarial Auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative HypergraphsACM Transactions on Information Systems10.1145/354410541:2(1-25)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1145/3544105
Kong CMeng DLiu TLi MLiu QZhou LChao PZhang Y(2022)SAME: Sampling Attack in Multiplex Network EmbeddingAdvanced Data Mining and Applications10.1007/978-3-031-22137-8_25(337-351)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-22137-8_25

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