How to network in online social networks

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object , External research report 01 Apr 2014Embargo end date: 01 Mar 2014 France Publisher:IEEEJournal:2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Authors: Neglia, Giovanni; Ye, Xiuhui; Gabielkov, Maksym; Legout, Arnaud;

doi: 10.1109/infcomw.2014.6849336 , 10.48550/arxiv.1403.0850

arXiv: 1403.0850

How to network in online social networks

- Summary
- Subjects
- Metrics

Abstract

In this paper, we consider how to maximize users' influence in Online Social Networks (OSNs) by exploiting social relationships only. Our first contribution is to extend to OSNs the model of Kempe et al. [1] on the propagation of information in a social network and to show that a greedy algorithm is a good approximation of the optimal algorithm that is NP-hard. However, the greedy algorithm requires global knowledge, which is hardly practical. Our second contribution is to show on simulations on the full Twitter social graph that simple and practical strategies perform close to the greedy algorithm.

NetSciCom 2014 - The Sixth IEEE International Workshop on Network Science for Communication Networks (2014)

Country

France

Related Organizations

View all View all

Keywords

Social and Information Networks (cs.SI), FOS: Computer and information sciences, influence, Physics - Physics and Society, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI], Twitter, FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), social network

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	3
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average