research-article

Cold start link prediction

Authors:

B. Barla Cambazoglu,

Francesco BonchiAuthors Info & Claims

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 393 - 402

https://doi.org/10.1145/1835804.1835855

Published: 25 July 2010 Publication History

Abstract

In the traditional link prediction problem, a snapshot of a social network is used as a starting point to predict, by means of graph-theoretic measures, the links that are likely to appear in the future. In this paper, we introduce cold start link prediction as the problem of predicting the structure of a social network when the network itself is totally missing while some other information regarding the nodes is available. We propose a two-phase method based on the bootstrap probabilistic graph. The first phase generates an implicit social network under the form of a probabilistic graph. The second phase applies probabilistic graph-based measures to produce the final prediction. We assess our method empirically over a large data collection obtained from Flickr, using interest groups as the initial information. The experiments confirm the effectiveness of our approach.

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Cited By

Peng HLi SZhao DZhong MQian CWang W(2024)Predicting hyperlinks via weighted hypernetwork loop structureThe European Physical Journal Special Topics10.1140/epjs/s11734-024-01145-y233:4(863-877)Online publication date: 14-Mar-2024
https://doi.org/10.1140/epjs/s11734-024-01145-y
Tran CShin WSpitz A(2024)IM-META: Influence Maximization Using Node Metadata in Networks With Unknown TopologyIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.336290311:3(3148-3160)Online publication date: May-2024
https://doi.org/10.1109/TNSE.2024.3362903
He WOuyang YPeng KRong WXiong Z(2024)Disentangled Hierarchical Attention Graph Neural Network for RecommendationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5663-6_35(415-426)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5663-6_35
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Index Terms

Cold start link prediction
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

July 2010

1240 pages

ISBN:9781450300551

DOI:10.1145/1835804

General Chairs:
Bharat Rao
Siemens
,
Balaji Krishnapuram
Siemens
,
Program Chairs:
Andrew Tomkins
Google Inc.
,
Qiang Yang
Hong Kong University of Science and Technology

Copyright © 2010 ACM.

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 ACM 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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Publication History

Published: 25 July 2010

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KDD '10

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KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 25 - 28, 2010

DC, Washington, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1140/epjs/s11734-024-01145-y
Tran CShin WSpitz A(2024)IM-META: Influence Maximization Using Node Metadata in Networks With Unknown TopologyIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.336290311:3(3148-3160)Online publication date: May-2024
https://doi.org/10.1109/TNSE.2024.3362903
He WOuyang YPeng KRong WXiong Z(2024)Disentangled Hierarchical Attention Graph Neural Network for RecommendationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5663-6_35(415-426)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5663-6_35
Lu JLin KChen RLin MChen XLu P(2023)Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanismBMC Medical Informatics and Decision Making10.1186/s12911-023-02152-023:1Online publication date: 6-Apr-2023
https://doi.org/10.1186/s12911-023-02152-0
Wang HLiu GHu P(2023)TDAN: Transferable Domain Adversarial Network for Link Prediction in Heterogeneous Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/361022918:1(1-22)Online publication date: 6-Sep-2023
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Zhang Z(2023)Heterogeneous Graph Neural Network Based on Hierarchical Attention Mechanism2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE)10.1109/ICEACE60673.2023.10441928(485-489)Online publication date: 29-Dec-2023
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Dong XZhang YPang KChen FLu M(2022)Heterogeneous graph neural networks with denoising for graph embeddingsKnowledge-Based Systems10.1016/j.knosys.2021.107899238:COnline publication date: 28-Feb-2022
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