Probabilistic topic and role model for information diffusion in social network

Hengpeng Xu, Jinmao Wei, Zhenglu Yang, Jianhua Ruan, Jun Wang

Resultado de la investigación: Conference contribution

3 Citas (Scopus)

Resumen

Information diffusion, which addresses the issue of how a piece of information spreads and reaches individuals in or between networks, has attracted considerable research attention due to its widespread applications, such as viral marketing and rumor control. However, the process of information diffusion is complex and its underlying mechanism remains unclear. An important reason is that social influence takes many forms and each form may be determined by various factors. One of the major challenges is how to capture all the crucial factors of a social network such as users’ interests (which can be represented as topics), users’ attributes (which can be summarized as roles), and users’ reposting behaviors in a unified manner to model the information diffusion process. To address the problem, we propose the joint information diffusion model (TRM) that integrates user topical interest extraction, role recognition, and information diffusion modeling into a unified framework. TRM seamlessly unifies the user topic role extraction, role recognition, and modeling of information diffusion, and then translates the calculations of individual level influence to the role-topic pairwise influence, which can provide a coarse-grained diffusion representation. Extensive experiments on two real-world datasets validate the effectiveness of our approach under various evaluation indices, which performs superior than the state-of-the-art models by a large margin.

Idioma originalEnglish (US)
Título de la publicación alojadaAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditoresBao Ho, Dinh Phung, Geoffrey I. Webb, Vincent S. Tseng, Mohadeseh Ganji, Lida Rashidi
EditorialSpringer Verlag
Páginas3-15
Número de páginas13
ISBN (versión impresa)9783319930367
DOI
EstadoPublished - 2018
Evento22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duración: jun. 3 2018jun. 6 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10938 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conference

Conference22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
País/TerritorioAustralia
CiudadMelbourne
Período6/3/186/6/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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