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Social action tracking via noise tolerant time-varying factor graphs

Authors:

Fengjiao WangAuthors Info & Claims

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

Pages 1049 - 1058

https://doi.org/10.1145/1835804.1835936

Published: 25 July 2010 Publication History

Abstract

It is well known that users' behaviors (actions) in a social network are influenced by various factors such as personal interests, social influence, and global trends. However, few publications systematically study how social actions evolve in a dynamic social network and to what extent different factors affect the user actions.

In this paper, we propose a Noise Tolerant Time-varying Factor Graph Model (NTT-FGM) for modeling and predicting social actions. NTT-FGM simultaneously models social network structure, user attributes and user action history for better prediction of the users' future actions. More specifically, a user's action at time t is generated by her latent state at t, which is influenced by her attributes, her own latent state at time t-1 and her neighbors' states at time t and t-1. Based on this intuition, we formalize the social action tracking problem using the NTT-FGM model; then present an efficient algorithm to learn the model, by combining the ideas from both continuous linear system and Markov random field.

Finally, we present a case study of our model on predicting future social actions. We validate the model on three different types of real-world data sets. Qualitatively, our model can uncover some interesting patterns of the social dynamics. Quantitatively, experimental results show that the proposed method outperforms several baseline methods for action prediction.

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Cited By

Sayari SHarounabadi ABanirostam T(2024)Community detection based on improved user interaction degree, weighted quasi-local path-based similarity and frequent pattern miningThe Journal of Supercomputing10.1007/s11227-024-06178-780:13(18544-18572)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06178-7
Li LZheng XHan JHao F(2022)Information cascades blocking through influential nodes identification on social networksJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-022-04456-x14:6(7519-7530)Online publication date: 7-Dec-2022
https://doi.org/10.1007/s12652-022-04456-x
Deng ZZheng XYe ZCai ZZeng D(2021)Credible Influence Analysis in Mass Media Using Causal Inference2021 IEEE International Conference on Intelligence and Security Informatics (ISI)10.1109/ISI53945.2021.9624679(1-5)Online publication date: 2-Nov-2021
https://doi.org/10.1109/ISI53945.2021.9624679
Show More Cited By

Index Terms

Social action tracking via noise tolerant time-varying factor graphs
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

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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Cited By

Sayari SHarounabadi ABanirostam T(2024)Community detection based on improved user interaction degree, weighted quasi-local path-based similarity and frequent pattern miningThe Journal of Supercomputing10.1007/s11227-024-06178-780:13(18544-18572)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06178-7
Li LZheng XHan JHao F(2022)Information cascades blocking through influential nodes identification on social networksJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-022-04456-x14:6(7519-7530)Online publication date: 7-Dec-2022
https://doi.org/10.1007/s12652-022-04456-x
Deng ZZheng XYe ZCai ZZeng D(2021)Credible Influence Analysis in Mass Media Using Causal Inference2021 IEEE International Conference on Intelligence and Security Informatics (ISI)10.1109/ISI53945.2021.9624679(1-5)Online publication date: 2-Nov-2021
https://doi.org/10.1109/ISI53945.2021.9624679
Che ZChang JZhang HDu F(2021)A Microblog Popularity Prediction Model Based on Temporal Sequence Features and Text Features2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI)10.1109/CEI52496.2021.9574577(795-800)Online publication date: 24-Sep-2021
https://doi.org/10.1109/CEI52496.2021.9574577
Guo LZhang DSilva T(2020)Measure User Intimacy by Mining Maximum Information Transmission PathsComplexity10.1155/2020/23764512020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/2376451
Byun JWoo SKim D(2020)ChronoGraph: Enabling Temporal Graph Traversals for Efficient Information Diffusion Analysis over TimeIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289156532:3(424-437)Online publication date: 1-Mar-2020
https://doi.org/10.1109/TKDE.2019.2891565
Loria EPirker JDrachen AMarconi A(2020)Do Influencers Influence? – Analyzing Players’ Activity in an Online Multiplayer Game2020 IEEE Conference on Games (CoG)10.1109/CoG47356.2020.9231957(120-127)Online publication date: Aug-2020
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Wang ZSun Z(2020)Can the adoption of health information on social media be predicted by information characteristics?Aslib Journal of Information Management10.1108/AJIM-12-2019-0369ahead-of-print:ahead-of-printOnline publication date: 8-Dec-2020
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Safari RRahmani AAlizadeh S(2019)User behavior mining on social media: a systematic literature reviewMultimedia Tools and Applications10.1007/s11042-019-08046-678:23(33747-33804)Online publication date: 17-Aug-2019
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Han DWei FBai LTang XZhu TWang G(2019)An Algorithm of Sina Microblog User’s Sentimental Influence Analysis Based on CNN+ELM ModelProceedings of ELM 201810.1007/978-3-030-23307-5_10(86-97)Online publication date: 30-Jun-2019
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