research-article

sonLP: social network link prediction by principal component regression

Authors:

Y. C. TayAuthors Info & Claims

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 364 - 371

https://doi.org/10.1145/2492517.2492558

Published: 25 August 2013 Publication History

Abstract

Social networks are driven by social interaction and therefore dynamic. When modeled as a graph, nodes and links are continually added and deleted, and there is considerable interest in social network analysis on predicting link formation. Current work has not adequately addressed three issues:

(1) Most link predictors start with using features from the link topology as input. How do features in other dimensions of the social network data affect link formation? (2) The dynamic nature of social networks implies the features driving link formation are constantly changing. How can a predictor automatically select the features that are important for link formation? (3) Node pairs that are not linked can outnumber links by orders of magnitude, but previous work do not address this imbalance. How can we design a predictor that is robust with respect to link imbalance?

This paper presents sonLP, a social network link predictor. It uses principal component analysis to identify features that are important to link prediction, its tradeoff between true and false positives is near optimal for a wide range of link imbalance, and it has optimal time complexity. Experiments with coauthorship prediction in the ACM researcher community also show the importance of using features outside the links' dimension.

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Cited By

Pujari MKanawati R(2020)Link Prediction in Complex NetworksCognitive Analytics10.4018/978-1-7998-2460-2.ch061(1196-1236)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2460-2.ch061
Gao MChen LLi BLiu W(2018)A link prediction algorithm based on low-rank matrix completionApplied Intelligence10.1007/s10489-018-1220-448:12(4531-4550)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-018-1220-4
Shakibian HCharkari NJalili S(2018)Multi-kernel one class link prediction in heterogeneous complex networksApplied Intelligence10.1007/s10489-018-1157-748:10(3411-3428)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10489-018-1157-7
Show More Cited By

Index Terms

sonLP: social network link prediction by principal component regression
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

Copyright © 2013 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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New York, NY, United States

Publication History

Published: 25 August 2013

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Ministry of Education - Singapore

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ASONAM '13

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SIGKDD

ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Citations

Cited By

Pujari MKanawati R(2020)Link Prediction in Complex NetworksCognitive Analytics10.4018/978-1-7998-2460-2.ch061(1196-1236)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2460-2.ch061
Gao MChen LLi BLiu W(2018)A link prediction algorithm based on low-rank matrix completionApplied Intelligence10.1007/s10489-018-1220-448:12(4531-4550)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-018-1220-4
Shakibian HCharkari NJalili S(2018)Multi-kernel one class link prediction in heterogeneous complex networksApplied Intelligence10.1007/s10489-018-1157-748:10(3411-3428)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10489-018-1157-7
Tajeuna EBouguessa MWang SShin SShin DLencastre M(2017)Survival analysis for modeling critical events that communities may undergo in dynamic social networksProceedings of the Symposium on Applied Computing10.1145/3019612.3019817(1068-1075)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3019612.3019817
Dai CChen LLi BLi Y(2017)Link prediction in multi-relational networks based on relational similarityInformation Sciences: an International Journal10.1016/j.ins.2017.02.003394:C(198-216)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.02.003
Gao MChen LLi BLi YLiu WXu Y(2017)Projection-based link prediction in a bipartite networkInformation Sciences: an International Journal10.1016/j.ins.2016.10.015376:C(158-171)Online publication date: 10-Jan-2017
https://dl.acm.org/doi/10.1016/j.ins.2016.10.015
Dai CChen LLi B(2017)Link prediction based on sampling in complex networksApplied Intelligence10.1007/s10489-016-0872-147:1(1-12)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10489-016-0872-1
Caiyan DChen LLi B(2017)Link prediction in complex network based on modularitySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2030-421:15(4197-4214)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s00500-016-2030-4
Pujari MKanawati R(2016)Link Prediction in Complex NetworksAdvanced Methods for Complex Network Analysis10.4018/978-1-4666-9964-9.ch003(58-97)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9964-9.ch003
Ahmed NChen LWang YLi BLi YLiu W(2016)Sampling-based algorithm for link prediction in temporal networksInformation Sciences: an International Journal10.1016/j.ins.2016.09.029374:C(1-14)Online publication date: 20-Dec-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.09.029
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