Geo-social influence spanning maximization

Jianxin Li, Timos Sellis, J. Shane Culpepper, Zhenying He, Chengfei Liu, Junhu Wang

Research output: Chapter in Book/Conference paperConference paperpeer-review

3 Citations (Scopus)


The problem of influence maximization has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the social users, although location is an important factor in targeted marketing. In this paper, we investigate the problem of influence spanning maximization in location-Aware social networks. Our target is to identify the maximum spanning geographical regions in a query region, which is very different from the existing methods that focus on the quantity of the activated users in the query region. Since the problem is NP-hard, we develop one greedy algorithm with a 1-1/e approximation ratio and further improve its efficiency by developing an upper bound based approach. Then, we propose the OIR index by combining ordered influential node lists and an R∗-Tree and design the index based solution. The efficiency and effectiveness of our proposed solutions and index have been verified using three real datasets.

Original languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages2
ISBN (Electronic)9781538655207
Publication statusPublished - 24 Oct 2018
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018


Conference34th IEEE International Conference on Data Engineering, ICDE 2018


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