Maximizing time-decaying influence in social networks

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

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

15 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
編集者Jilles Giuseppe, Niels Landwehr, Giuseppe Manco, Paolo Frasconi
出版社Springer Verlag
ページ132-147
ページ数16
ISBN(印刷版)9783319461274
DOI
出版ステータス出版済み - 2016
外部発表はい
イベント15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, イタリア
継続期間: 9月 19 20169月 23 2016

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9851 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
国/地域イタリア
CityRiva del Garda
Period9/19/169/23/16

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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