research-article

Unsupervised Ranking using Graph Structures and Node Attributes

Authors:

Shou-De LinAuthors Info & Claims

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

Pages 771 - 779

https://doi.org/10.1145/3018661.3018668

Published: 02 February 2017 Publication History

Abstract

PageRank has been the signature unsupervised ranking model for ranking node importance in a graph. One potential drawback of PageRank is that its computation depends only on input graph structures, not considering external information such as the attributes of nodes. This work proposes AttriRank, an unsupervised ranking model that considers not only graph structure but also the attributes of nodes. AttriRank is unsupervised and domain-independent, which is different from most of the existing works requiring either ground-truth labels or specific domain knowledge. Combining two reasonable assumptions about PageRank and node attributes, AttriRank transfers extra node information into a Markov chain model to obtain the ranking. We further develop approximation for AttriRank and reduce its complexity to be linear to the number of nodes or links in the graph, which makes it feasible for large network data. The experiments show that AttriRank outperforms competing models in diverse graph ranking applications.

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Cited By

Muro CLi BHe K(2024)LUSTC: A Novel Approach for Predicting Link States on Dynamic Attributed NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325226111:1(1075-1085)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3252261
Chen TMa HLi J(2024)Incorporating Attribute Information into Joint Random Walk for Interpretable Local Community Detection2024 10th International Conference on Systems and Informatics (ICSAI)10.1109/ICSAI65059.2024.10893824(1-9)Online publication date: 14-Dec-2024
https://doi.org/10.1109/ICSAI65059.2024.10893824
Liu XLi X(2024)Unbiased evaluation of ranking algorithms applied to the Chinese green patents citation networkScientometrics10.1007/s11192-024-05023-1129:6(2999-3021)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11192-024-05023-1
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Index Terms

Unsupervised Ranking using Graph Structures and Node Attributes
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Random walks and Markov chains
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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cover image ACM Conferences

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

February 2017

868 pages

ISBN:9781450346757

DOI:10.1145/3018661

General Chairs:
Maarten de Rijke
University of Amsterdam
,
Milad Shokouhi
Microsoft
,
Program Chairs:
Andrew Tomkins
Google
,
Min Zhang
Tsinghua University

Copyright © 2017 ACM.

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Published: 02 February 2017

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WSDM 2017

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WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining

February 6 - 10, 2017

Cambridge, United Kingdom

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WSDM '17 Paper Acceptance Rate 80 of 505 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Muro CLi BHe K(2024)LUSTC: A Novel Approach for Predicting Link States on Dynamic Attributed NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325226111:1(1075-1085)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3252261
Chen TMa HLi J(2024)Incorporating Attribute Information into Joint Random Walk for Interpretable Local Community Detection2024 10th International Conference on Systems and Informatics (ICSAI)10.1109/ICSAI65059.2024.10893824(1-9)Online publication date: 14-Dec-2024
https://doi.org/10.1109/ICSAI65059.2024.10893824
Liu XLi X(2024)Unbiased evaluation of ranking algorithms applied to the Chinese green patents citation networkScientometrics10.1007/s11192-024-05023-1129:6(2999-3021)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11192-024-05023-1
Ghanbari MBagloee SQi JSarvi M(2024)Feature-Aware Unsupervised Detection of Important Nodes in GraphsAdvanced Data Mining and Applications10.1007/978-981-96-0821-8_7(98-113)Online publication date: 15-Dec-2024
https://doi.org/10.1007/978-981-96-0821-8_7
Abbas KHasan MAbbasi AMokhtar UKhan AAbdullah SDong SIslam SAlboaneen DAhmed F(2023)Predicting the Future Popularity of Academic Publications Using Deep Learning by Considering It as Temporal Citation NetworksIEEE Access10.1109/ACCESS.2023.329090611(83052-83068)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3290906
Park JTran CShin WCao XAl Hasan MXiong L(2022)GradAlign+: Empowering Gradual Network Alignment Using Attribute AugmentationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557605(4374-4378)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557605
Xie TMa YKang JTong HMaciejewski R(2022)FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining ModelsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311485028:1(368-377)Online publication date: Jan-2022
https://doi.org/10.1109/TVCG.2021.3114850
Yue HLiu HChen J(2022) A Gospel for MOBA Game: Ranking-Preserved Hero Change Prediction in Dota 2 IEEE Transactions on Games10.1109/TG.2021.312358314:2(191-201)Online publication date: Jun-2022
https://doi.org/10.1109/TG.2021.3123583
Li JMa H(2022)Attributed community search based on seed replacement and joint random walkAdvances in Computational Intelligence10.1007/s43674-022-00041-z2:5Online publication date: 1-Sep-2022
https://doi.org/10.1007/s43674-022-00041-z
Zhang XMa HGao ZLi ZChang L(2022)Enhancing Session-Based Recommendation with Global Context Information and Knowledge GraphDatabase Systems for Advanced Applications10.1007/978-3-031-00126-0_20(281-288)Online publication date: 8-Apr-2022
https://doi.org/10.1007/978-3-031-00126-0_20
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