Scalable and Parallel Processing of Influence Maximization for Large-Scale Social Networks

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2017 3rd International Conference on Big Data Computing and Communications, BigCom 2017
Subtitle of host publicationProceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages183-192
ISBN (Electronic)9781538633496
ISBN (Print)9781538633502
Publication statusPublished - 2017

Conference

Title3rd International Conference on Big Data Computing and Communications (BigCom 2017)
LocationInternational Conference Center
PlaceChina
CityChengdu
Period10 - 11 August 2017

Abstract

Influence maximization is a problem of finding a small subset of nodes as seeds in a social network such that the total influence of this subset of nodes for disseminating a message in the social network can be maximized. The problem has been extensively investigated in recent years and many influence maximization algorithms have been proposed. However, all of the existing algorithms are sequentially executed algorithms. It would take long time if they run on large-scale social networks. In this paper, we study parallel algorithms for two influence maximization problems in large-scale social networks: influence maximization without budget limitation and influence maximization with limited budget. We propose two parallel algorithms, Community-based Max Degree (CMD) algorithm and Max Degree Cost Ratio (MDCR) algorithm, respectively for the two problems. Both algorithms can run in parallel on Hadoop platform. Experiments are conducted for various sizes of social networks. The results show that our algorithms are scalable and outperform the common heuristic algorithms.

Research Area(s)

  • Hadoop, Influence Maximization, Parallel Algorithm, Social Network

Citation Format(s)

Scalable and Parallel Processing of Influence Maximization for Large-Scale Social Networks. / Chang, Yafei; Huang, Hejiao; Liu, Qin; Jia, Xiaohua.

2017 3rd International Conference on Big Data Computing and Communications, BigCom 2017: Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 183-192 8113065.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review