dc.contributor.author | Li, Jianxin | |
dc.contributor.author | Sellis, Timos | |
dc.contributor.author | Culpepper, J Shane | |
dc.contributor.author | He, Zhenying | |
dc.contributor.author | Liu, Chengfei | |
dc.contributor.author | Wang, Junhu | |
dc.date.accessioned | 2020-04-21T01:35:34Z | |
dc.date.available | 2020-04-21T01:35:34Z | |
dc.date.issued | 2018 | |
dc.identifier.isbn | 9781538655207 | |
dc.identifier.issn | 1084-4627 | |
dc.identifier.doi | 10.1109/ICDE.2018.00245 | |
dc.identifier.uri | http://hdl.handle.net/10072/393302 | |
dc.description.abstract | 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. | |
dc.language | English | |
dc.publisher | IEEE | |
dc.relation.ispartofconferencename | 34th IEEE International Conference on Data Engineering Workshops (ICDEW) | |
dc.relation.ispartofconferencetitle | 2018 IEEE 34th International Conference on Data Engineering (ICDE) | |
dc.relation.ispartofdatefrom | 2018-04-16 | |
dc.relation.ispartofdateto | 2018-04-19 | |
dc.relation.ispartoflocation | Paris, France | |
dc.relation.ispartofpagefrom | 1775 | |
dc.relation.ispartofpageto | 1776 | |
dc.subject.keywords | Science & Technology | |
dc.subject.keywords | Computer Science, Information Systems | |
dc.subject.keywords | Computer Science, Theory & Methods | |
dc.subject.keywords | Computer Science | |
dc.title | Geo-social Influence Spanning Maximization | |
dc.type | Conference output | |
dc.type.description | E3 - Conferences (Extract Paper) | |
dcterms.bibliographicCitation | Li, J; Sellis, T; Culpepper, JS; He, Z; Liu, C; Wang, J, Geo-social Influence Spanning Maximization, 2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018, pp. 1775-1776 | |
dc.date.updated | 2020-04-21T01:32:46Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Wang, John | |