Tracking the evolution of community structures in time-evolving social networks

Tajeuna, Etienne Gael; Bouguessa, Mohamed; Wang, Shengrui

doi:10.1109/dsaa.2015.7344876

Cited by 20 publications

(18 citation statements)

References 14 publications

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“…For example, the communities C 1 and C 2 can be considered similar if they have at least 30% of their nodes in common. Note that the threshold criterion may be at the users discretion, or it may also be automatically set, as in [4], [7].…”

Section: A Concept Definitionsmentioning

confidence: 99%

“…For instance, S C a = {C a t1 , C a t2 , C a t3 , ..., C a t9 } will be the evolution of community C a from t 1 till t 9 . After identifying the communities over time, the authors in [4], [5], [6], [7], [13], [14] perform a pairwise comparison between the identified community structures. Here, we note that the comparison is performed by investigating the similarity of two communities identified at two consecutive or non-consecutive timestamps.…”

Section: A Concept Definitionsmentioning

confidence: 99%

“…Tajeuna et al [7]: In order to track communities and extract critical events, Tajeuna et al first represented each community as a vector (called the transition probability vector) indicating the number of nodes shared by the communities over time. Then, they compared the corresponding vectors of different communities.…”

Section: )mentioning

confidence: 99%

“…Recent methods [3], [4], [5], model the graph as a series of frozen networks, where each network corresponds to a particular point in time. Such modeling has been useful to detect structural changes in the network [4], [5], [6], [7], [8] and to reveal important network information. To detect changes in dynamic social networks, two major approaches have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Some authors use a global approach [3], [9], [10], in which the complete network is tracked over time to observe how nodes and edges behave. Others [4], [5], [7], [8] focus their efforts on tracking communities over time. In this paper, our focus is on the evolution of communities over time.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks

He¹,

Tajeuna

Wang

et al. 2017

2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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In real-world social networks, there is an increasing interest in tracking the evolution of groups of users and detecting the various changes they are liable to undergo. Several approaches have been proposed for this. In studying these approaches, we observed that most of them use a two-stage process. In the first stage, they run an algorithm to identify groups of users at each timestamp. In the second stage, a pairwise comparison based on a similarity measure is employed to track groups of users and detect changes they may undergo. While the majority of existing approaches use a two-stage process, they all run different algorithms to identify communities and rely on different similarity measures to track groups of users over time. Noting that the different approaches may perform differently depending on the dynamic social network under investigation, we decided to make a high level survey of some existing tracking approaches and then do a comparative analysis of some of them. In our analysis, we compared the algorithms in two main situations: (1) when groups of users do not overlap and (2) when the groups are overlapping. The study was done on three different testbeds extracted from the DBLP, Autonomous System (AS) and Yelp datasets.

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Section: A Concept Definitionsmentioning

confidence: 99%