research-article

Social influence analysis in large-scale networks

Authors:

Zi YangAuthors Info & Claims

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 807 - 816

https://doi.org/10.1145/1557019.1557108

Published: 28 June 2009 Publication History

Abstract

In large social networks, nodes (users, entities) are influenced by others for various reasons. For example, the colleagues have strong influence on one's work, while the friends have strong influence on one's daily life. How to differentiate the social influences from different angles(topics)? How to quantify the strength of those social influences? How to estimate the model on real large networks?

To address these fundamental questions, we propose Topical Affinity Propagation (TAP) to model the topic-level social influence on large networks. In particular, TAP can take results of any topic modeling and the existing network structure to perform topic-level influence propagation. With the help of the influence analysis, we present several important applications on real data sets such as 1) what are the representative nodes on a given topic? 2) how to identify the social influences of neighboring nodes on a particular node?

To scale to real large networks, TAP is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework. We further present the common characteristics of distributed learning algorithms for Map-Reduce. Finally, we demonstrate the effectiveness and efficiency of TAP on real large data sets.

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Index Terms

Social influence analysis in large-scale networks
1. Information systems
  1. Information retrieval
  2. Information systems applications

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cover image ACM Conferences

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

June 2009

1426 pages

ISBN:9781605584959

DOI:10.1145/1557019

General Chairs:
John Elder
Elder Research, Inc., USA
,
Françoise Soulié Fogelman
KXEN, France
,
Program Chairs:
Peter Flach
University of Bristol, UK
,
Mohammed Zaki
RPI, USA

Copyright © 2009 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: 28 June 2009

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28 - July 1, 2009

Paris, France

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

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https://dl.acm.org/doi/10.1145/3701551.3703487
Xie XChen WKang Z(2025)Robust graph structure learning under heterophilyNeural Networks10.1016/j.neunet.2025.107206185(107206)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107206
Quan PZheng LZhang WXiao YNiu LShi Y(2025)ExGAT: Context extended graph attention neural networkNeural Networks10.1016/j.neunet.2024.106784181(106784)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106784
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