research-article

PRRE: Personalized Relation Ranking Embedding for Attributed Networks

Authors:

Can WangAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 823 - 832

https://doi.org/10.1145/3269206.3271741

Published: 17 October 2018 Publication History

Abstract

Attributed network embedding focuses on learning low-dimensional latent representations of nodes which can well preserve the original topological and node attributed proximity at the same time. Existing works usually assume that nodes with similar topology or similar attributes should also be close in the embedding space. This assumption ignores the phenomenon of partial correlation between network topological and node attributed similarities i.e. nodes with similar topology may be dissimilar in their attributes and vice versa. Partial correlation between the two information sources should be considered especially when there exist fraudulent edges (i.e., information from one source is vague) or unbalanced data distributions (i.e, topology structure similarity and node attribute similarity have different distributions). However, it is very challenging to consider the partial correlation between topology and attributes due to the heterogeneity of these two information sources. In this paper, we take partial correlation between topology and attributes into account and propose the Personalized Relation Ranking Embedding (PRRE) method for attributed networks which is capable of exploiting the partial correlation between node topology and attributes. The proposed PRRE model utilizes two thresholds to define different node relations and employs the Expectation-Maximization (EM) algorithm to learn these thresholds as well as other embedding parameters. Extensive experiments results on multiple real-world datasets show that the proposed PRRE model significantly outperforms the state-of-the-art methods in terms of various evaluation metrics.

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Cited By

Xie KDong XZhang YZhang XGuo QWang S(2024)Learning-Based Attribute-Augmented Proximity Matrix Factorization for Attributed Network EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338584736:11(6517-6531)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3385847
Wu AYuan YLi CMa YZhang H(2024)Attributed Network Embedding in Streaming Style2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00243(3138-3150)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00243
Huang TZhou LLü KWang LChen HDu G(2023)An Empirical Evaluation of Algorithms for Link PredictionInformation Systems Frontiers10.1007/s10796-023-10440-3Online publication date: 1-Nov-2023
https://doi.org/10.1007/s10796-023-10440-3
Show More Cited By

Index Terms

PRRE: Personalized Relation Ranking Embedding for Attributed Networks
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Published: 17 October 2018

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Alibaba-Zhejiang University Joint Institute of Frontier Technologies and Zhejiang Provincial Key Research and Development Plan
National Key Technology R\&D Program of China
China Postdoctoral Science Foundation

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CIKM '18

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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

Xie KDong XZhang YZhang XGuo QWang S(2024)Learning-Based Attribute-Augmented Proximity Matrix Factorization for Attributed Network EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338584736:11(6517-6531)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3385847
Wu AYuan YLi CMa YZhang H(2024)Attributed Network Embedding in Streaming Style2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00243(3138-3150)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00243
Huang TZhou LLü KWang LChen HDu G(2023)An Empirical Evaluation of Algorithms for Link PredictionInformation Systems Frontiers10.1007/s10796-023-10440-3Online publication date: 1-Nov-2023
https://doi.org/10.1007/s10796-023-10440-3
Chen LLi YDeng XLiu CHe TXiao R(2023)Adversarial enhanced attributed network embeddingKnowledge and Information Systems10.1007/s10115-023-01980-w66:2(1301-1336)Online publication date: 6-Oct-2023
https://doi.org/10.1007/s10115-023-01980-w
Yang RShi JXiao XYang YBhowmick SLiu J(2023)PANE: scalable and effective attributed network embeddingThe VLDB Journal10.1007/s00778-023-00790-432:6(1237-1262)Online publication date: 24-Mar-2023
https://doi.org/10.1007/s00778-023-00790-4
Yang RShi JXiao XYang YBhowmick SLiu J(2022)No PANE, No GainACM SIGMOD Record10.1145/3542700.354271151:1(42-49)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1145/3542700.3542711
Wang CZhou SYu KChen DLi BFeng YChen C(2022)Collaborative Knowledge Distillation for Heterogeneous Information Network EmbeddingProceedings of the ACM Web Conference 202210.1145/3485447.3512209(1631-1639)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512209
Zheng XZhao GZhu LZhu JQian X(2022)What You Like, What I Am: Online Dating Recommendation via Matching Individual Preferences with FeaturesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3148485(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3148485
Dong KZhou LHuang TDu GJiang Y(2022)Attributed network embedding with dual fusion strategiesJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2022.215327036:7(1307-1330)Online publication date: 22-Dec-2022
https://doi.org/10.1080/0952813X.2022.2153270
Fan GGeng BTao JWang KFan CZeng W(2022)PPPNENeurocomputing10.1016/j.neucom.2021.11.059472:C(103-112)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.neucom.2021.11.059
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