Sequential detection of temporal communities by estrangement confinement

Kawadia, Vikas; Sreenivasan, Sameet

doi:10.1038/srep00794

Cited by 45 publications

(51 citation statements)

References 27 publications

(57 reference statements)

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“…In this subsection, we report the results of performing community detection on the two real dynamic datasets introduced in Subsection IV-A by using the dynamic community detection algorithms, LabelRankT [20] and Estrangement [17]. LabelRankT [20] detects communities in large-scale dynamic networks through stabilized label propagation.…”

Section: Resultsmentioning

confidence: 99%

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On Measuring the Quality of a Network Community Structure

Chen

Nguyen

Szymanski

2013

2013 International Conference on Social Computing

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Abstract-Modularity is widely used to effectively measure the strength of the community structure found by community detection algorithms. However, modularity maximization suffers from two opposite yet coexisting problems: in some cases, it tends to favor small communities over large ones while in others, large communities over small ones. The latter tendency is known in the literature as the resolution limit problem. To address them, we propose to modify modularity by subtracting from it the fraction of edges connecting nodes of different communities and by including community density into modularity. We refer to the modified metric as Modularity Density and we demonstrate that it indeed resolves both problems mentioned above. We describe the motivation for introducing this metric by using intuitively clear and simple examples. We also discuss the results of applying this metric, modularity, and several other popular community quality metrics to two real dynamic networks. The results imply that Modularity Density is consistent with all the community quality measurements but not modularity, which suggests that Modularity Density is an improved measurement of the community quality compared to modularity.

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Section: Resultsmentioning

confidence: 99%

“…Senate Dataset [17], [18]. The Senate dataset is a timeevolving weighted network comprised of United States senators where the weight of an edge represents the similarity of their roll call voting behavior.…”

Section: A Real Dynamic Datasetsmentioning

confidence: 99%

On Measuring the Quality of a Network Community Structure

Chen

Nguyen

Szymanski

2013

2013 International Conference on Social Computing

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“…As a baseline for comparison, we applied three wellknown community mining algorithms of different approaches. These methods are FacetNet [16] , Estrangement [34] and static clustering using FUA method [4]. The static method does not consider any temporal evolution and is applied on each snapshot independently.…”

Section: Methodsmentioning

confidence: 99%

“…In their proposed framework, the community structure of the current snapshot is detected by incorporating the current graph structure and historic community evolution patterns [33]. Recently, Kawadia and Sreenivasan [34] have proposed a new measure called "Estrangement" to quantify the partition distance between two consecutive timesteps. They describe estrangement as "the fraction of intracommunity edges that become inter-community edges as the network evolves to the subsequent snapshot".…”

Section: B Community Detection In Dynamic Networkmentioning

confidence: 99%

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Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Javadi

Gharani

Khadivi

2018

Studies in Systems, Decision and Control

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Abstract-Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of realworld social networks is the presence of leaders, i.e. nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method is able to find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature, enables us to efficiently detect and track communities over time rapidly. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks.

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Context‐aided analysis of community evolution in networks

Pallotta

Konjevod

Cadena

et al. 2017

Statistical Analysis

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We are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure based on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label‐unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. We offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.

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Sequential detection of temporal communities by estrangement confinement

Cited by 45 publications

References 27 publications

On Measuring the Quality of a Network Community Structure

On Measuring the Quality of a Network Community Structure

Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Context‐aided analysis of community evolution in networks

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