research-article

Who will follow you back?: reciprocal relationship prediction

Authors:

Jie TangAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 1137 - 1146

https://doi.org/10.1145/2063576.2063740

Published: 24 October 2011 Publication History

Abstract

We study the extent to which the formation of a two-way relationship can be predicted in a dynamic social network. A two-way (called reciprocal) relationship, usually developed from a one-way (parasocial) relationship, represents a more trustful relationship between people. Understanding the formation of two-way relationships can provide us insights into the micro-level dynamics of the social network, such as what is the underlying community structure and how users influence each other. Employing Twitter as a source for our experimental data, we propose a learning framework to formulate the problem of reciprocal relationship prediction into a graphical model. The framework incorporates social theories into a machine learning model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a dynamic network. Our study provides strong evidence of the existence of the structural balance among reciprocal relationships. In addition, we have some interesting findings, e.g., the likelihood of two "elite" users creating a reciprocal relationships is nearly 8 times higher than the likelihood of two ordinary users. More importantly, our findings have potential implications such as how social structures can be inferred from individuals' behaviors.

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Cited By

Zhang ZZhu ZZhang HLi C(2025)Exploring Behavioral Tendencies on Social Media: A Perspective Through Claim Check-WorthinessSocial Networks Analysis and Mining10.1007/978-3-031-78541-2_23(373-390)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-78541-2_23
Rabiee HLadani BSahafizadeh E(2024)A Social Network Model for Analysis of Public Opinion Formation ProcessIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.343590811:6(7698-7710)Online publication date: Dec-2024
https://doi.org/10.1109/TCSS.2024.3435908
Chen ZShao WLiu W(2024)Range constrained group query on attribute social graphDistributed and Parallel Databases10.1007/s10619-024-07439-342:3(337-375)Online publication date: 30-Mar-2024
https://doi.org/10.1007/s10619-024-07439-3
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Index Terms

Who will follow you back?: reciprocal relationship prediction
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

Copyright © 2011 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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Published: 24 October 2011

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CIKM '11

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October 24 - 28, 2011

Glasgow, Scotland, UK

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

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Cited By

Zhang ZZhu ZZhang HLi C(2025)Exploring Behavioral Tendencies on Social Media: A Perspective Through Claim Check-WorthinessSocial Networks Analysis and Mining10.1007/978-3-031-78541-2_23(373-390)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-78541-2_23
Rabiee HLadani BSahafizadeh E(2024)A Social Network Model for Analysis of Public Opinion Formation ProcessIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.343590811:6(7698-7710)Online publication date: Dec-2024
https://doi.org/10.1109/TCSS.2024.3435908
Chen ZShao WLiu W(2024)Range constrained group query on attribute social graphDistributed and Parallel Databases10.1007/s10619-024-07439-342:3(337-375)Online publication date: 30-Mar-2024
https://doi.org/10.1007/s10619-024-07439-3
Rasool SJain SMoon A(2024)A Graph Based-Novel Framework for Social Synchrony Detection Using Influential User and Event Detection ApproachCongress on Smart Computing Technologies10.1007/978-981-97-5081-8_37(491-506)Online publication date: 30-Oct-2024
https://doi.org/10.1007/978-981-97-5081-8_37
Zhang WLai XWang J(2023)Social Link Inference via Multiview Matching Network From Spatiotemporal TrajectoriesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.298647234:4(1720-1731)Online publication date: Apr-2023
https://doi.org/10.1109/TNNLS.2020.2986472
Huang XLi LWang HHu CXu XWu C(2022)Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social NetworksComplexity10.1155/2022/20729502022Online publication date: 1-Jan-2022
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Wu ZZhou JWang JSun X(2022)Wb-MSF: A Large-scale Multi-source Information Diffusion Dataset for Social Information Diffusion Prediction2022 Tenth International Conference on Advanced Cloud and Big Data (CBD)10.1109/CBD58033.2022.00023(79-84)Online publication date: Nov-2022
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Bansal MSharma D(2022)Density-based structural embedding for anomaly detection in dynamic networksNeurocomputing10.1016/j.neucom.2022.05.109500:C(724-740)Online publication date: 21-Aug-2022
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Adnan TIslam MPapon TNath SAdnan M(2022)UACD: A Local Approach for Identifying the Most Influential Spreaders in Twitter in a Distributed EnvironmentSocial Network Analysis and Mining10.1007/s13278-022-00862-312:1Online publication date: 20-Feb-2022
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Lin RTang YYuan CHe CLi W(2022)SCHOLAT Link Prediction: A Link Prediction Dataset Fusing Topology and Attribute InformationComputer Supported Cooperative Work and Social Computing10.1007/978-981-19-4549-6_26(340-351)Online publication date: 22-Jul-2022
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