Influential sustainability on social networks

Chien Wei Chang, Po An Yang, Ming Han Lyu, Kun Ta Chuang

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

2 Citations (Scopus)

Abstract

In this paper, we study a novel paradigm of viral marketing with the goal to sustain the influential effectiveness in the network. We study from real cases such as the Ice Bucket Challenges for the ALS awareness, and figure out the "easy come and easy go" phenomenon in the marketing promotion. Such a natural property is fully unexplored in the literature, but it will violate the need of many marketing applications which attempt to receive the perpetual attention and support. We thus highlight the problem of Influential Sustainability, to pursue the long-term and effective influence on the network. Given the set of initial seeds S and a threshold ρ, the goal of Influential Sustainability is to best decide the timing to activate each seed in S so as to maximize the number of iterations in which each iteration will activate the number of inactive nodes more than ρ. The Influential Sustainability problem is challenging due to its #P-hard nature. In addition to the greedy idea, we further present three strategies to heuristically decide the activating timing for each seed. As demonstrated in the empirical study on real data, instead of only providing the flexibility of striking a compromise between the execution efficiency and the resulting quality, these heuristic algorithms can be executed highly efficiently and meanwhile it is able to sustain the longer period which can continuously activate inactive nodes effectively. The results demonstrate their prominent advantage to be practical algorithms for the promising viral marketing paradigm.

Original languageEnglish
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages31-40
Number of pages10
ISBN (Electronic)9781467395038
DOIs
Publication statusPublished - 2016 Jan 5
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: 2015 Nov 142015 Nov 17

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Other

Other15th IEEE International Conference on Data Mining, ICDM 2015
CountryUnited States
CityAtlantic City
Period15-11-1415-11-17

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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