Maximizing time-decaying influence in social networks

Naoto Ohsaka, Yutaro Yamaguchi, Naonori Kakimura, Ken Ichi Kawarabayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)


Influence maximization is a well-studied problem of finding a small set of highly influential individuals in a social network such that the spread of influence under a certain diffusion model is maximized. We propose new diffusion models that incorporate the time-decaying phenomenon by which the power of influence decreases with elapsed time. In standard diffusion models such as the independent cascade and linear threshold models, each edge in a network has a fixed power of influence over time. However, in practical settings, such as rumor spreading, it is natural for the power of influence to depend on the time influenced. We generalize the independent cascade and linear threshold models with time-decaying effects. Moreover, we show that by using an analysis framework based on submodular functions, a natural greedy strategy obtains a solution that is provably within (1 − 1/e) of optimal. In addition, we propose theoretically and practically fast algorithms for the proposed models. Experimental results show that the proposed algorithms are scalable to graphs with millions of edges and outperform baseline algorithms based on a state-of-the-art algorithm.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
EditorsJilles Giuseppe, Niels Landwehr, Giuseppe Manco, Paolo Frasconi
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783319461274
Publication statusPublished - 2016
Externally publishedYes
Event15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italy
Duration: 2016 Sept 192016 Sept 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9851 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
CityRiva del Garda

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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