Local Community Detection and Visualization: Experiment Based on Student Data

Kudělka, Miloš; Dráždilová, Pavla; Ochodková, Eliška; Slaninová, Kateřina; Horák, Zdeněk

doi:10.1007/978-3-642-31603-6_25

Cited by 6 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of this paper is to study dependencies of sources in social networks. Information about sources' dependencies in a social network can be used to detect related groups, communities [1], . .…”

Section: Introductionmentioning

confidence: 99%

Independence of Sources in Social Networks

Chehibi

Chebbah

Martin

2018

Communications in Computer and Information Science

View full text Add to dashboard Cite

Online social networks are more and more studied. The links between users of a social network are important and have to be well qualified in order to detect communities and find influencers for example. In this paper, we present an approach based on the theory of belief functions to estimate the degrees of cognitive independence between users in a social network. We experiment the proposed method on a large amount of data gathered from the Twitter social network.

show abstract

Section: Introductionmentioning

confidence: 99%

Independence of Sources in Social Networks

Chehibi

Chebbah

Martin

2018

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…We presented our approach for local community detection in the paper [1]. There is a described algorithm, which use dependency of a vertex as a measure.…”

Section: A Communities Extraction Based On Vertex Dependency In the mentioning

confidence: 99%

“…input : social network G, start vertex n 0 input : empty community core C, empty community shell S input : community boundary B, equal to community base output: community L, L = C ∩ B Add n 0 to B, add all neighbours of n 0 to S while at least 1 vertex from S has been recognized do Community detection algorithm is described in the greater detail in our paper [1].…”

Section: Algorithm For Communities Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of head experts in social network

Radvansky

Kudělka

Snášel

2012

2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN)

View full text Add to dashboard Cite

This paper introduces a method for head expert identification in a social network based on local community detection and formal concept analysis. There are several methods for expert identification, but most of these methods try to find an expert for a particular area. In this paper, we propose a novel approach to identify a head expert. This person is in the background and most of the time he is working across different areas of research, and he can establish teams of experts for particular areas.

show abstract

“…However, the independence was never studied from the influence point of view. Kudelka et al [4] proposed to quantify the dependence between vertices of an OSN considered as a network in the aim of community detection. Chehibi et al [1] proposed a dependence measure for Twitter.…”

Section: Introductionmentioning

confidence: 99%

Evidential Independence Maximization on Twitter Network

Jendoubi

Chebbah

Martin

2018

Belief Functions: Theory and Applications

View full text Add to dashboard Cite

Detecting independent users in online social networks is an interesting research issue. In fact, independent users cannot generally be influenced, they are independent in their choices and decisions. Independent users may attract other users and make them adopt their point of view. A user is qualified as independent when his/her point of view does not depend on others ideas. Thus, the behavior of such a user is independent from other behaviors. Detecting independent users is interesting because a part of them can be influencers. Independent users that are not influencers can be directly targeted as they cannot be influenced. In this paper, we present an evidential independence maximization approach for Twitter users. The proposed approach is based on three metrics reflecting users behaviors. We propose an useful approach for detecting influencers. Indeed, we consider the independence as a characteristic of influencers even if not all independent users are influencers. The proposed approach is experimented on real data crawled from Twitter.

show abstract

Local Community Detection and Visualization: Experiment Based on Student Data

Cited by 6 publications

References 17 publications

Independence of Sources in Social Networks

Independence of Sources in Social Networks

Detection of head experts in social network

Evidential Independence Maximization on Twitter Network

Contact Info

Product

Resources

About