An event-based framework for characterizing the evolutionary behavior of interaction graphs

Asur, Sitaram; Parthasarathy, S.; Ucar, Duygu

doi:10.1145/1631162.1631164

Cited by 223 publications

(167 citation statements)

References 34 publications

(48 reference statements)

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“…to quantify whether the community decomposition of the graph observed at the beginning remains stable over time or evolves towards a different partition at the end. Other research has generally agreed on the main types of event that may occur as communities change over time [43][44][45][46]. We take the definitions of Asur et al [43] and denoting the set of nodes making up community C k in snapshot i by C • κ-Split: C k i has been split in snapshot i+1 if κ% of nodes in C k i are present in different communities in snapshot i + 1.…”

Section: Community Eventsmentioning

confidence: 99%

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Social and place-focused communities in location-based online social networks

et al. 2013

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Thanks to widely available, cheap Internet access and the ubiquity of smartphones, millions of people around the world now use online location-based social networking services. Understanding the structural properties of these systems and their dependence upon users' habits and mobility has many potential applications, including resource recommendation and link prediction. Here, we construct and characterise social and place-focused graphs by using longitudinal information about declared social relationships and about users' visits to physical places collected from a popular online location-based social service. We show that although the social and place-focused graphs are constructed from the same data set, they have quite different structural properties. We find that the social and location-focused graphs have different global and meso-scale structure, and in particular that social and place-focused communities have negligible overlap. Consequently, group inference based on community detection performed on the social graph alone fails to isolate placefocused groups, even though these do exist in the network. By studying the evolution of tie structure within communities, we show that the time period over which location data are aggregated has a substantial impact on the stability of place-focused communities, and that information about placebased groups may be more useful for user-centric applications than that obtained from the analysis of social communities alone.Networks can describe a large variety of complex systems, and network science has proved to be a successful framework for the quantitative study of their structure and dynamics [1][2][3]. In the last decade, the tools and models provided by complex network theory have enabled discovery of similarities between seemingly very different systems including the Internet, the human proteome, and collaboration networks. Complex networks analysis is now regularly employed to characterise the topology and functioning of biological, technological and social structures [4,5].The analysis of social networks is one of the traditional application fields of network science, and sociologists generally agree that many social behaviours, from opinion formation to rule enforcement, from individual success to cooperation, depend in a fundamental way on the structure and evolution of the patterns of social relationships. In other words, characterising and quantifying social structures is often a prerequisite for understanding and interpreting social dynamics [6][7][8]. In the last twenty years, sociologists have relied on the study of small social networks with tens or hundreds of nodes at most, collected by means of targeted questionnaires and direct interviews. Recently, the ubiquity of the Internet and the World Wide Web, and the emergence of hundreds of online social services, have produced a huge volume of data about online relationships between millions of people around the world. These online social networks (OSNs) have allowed quantitative verification of sociologi...

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Section: Community Eventsmentioning

confidence: 99%

“…We take the definitions of Asur et al [43] and denoting the set of nodes making up community C k in snapshot i by C • κ-Split: C k i has been split in snapshot i+1 if κ% of nodes in C k i are present in different communities in snapshot i + 1. For Split and Merge we take κ = 50 as in [43].…”

Section: Community Eventsmentioning

confidence: 99%

Social and place-focused communities in location-based online social networks

et al. 2013

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“…Following the framework of evolutionary clustering, Chi et al (2007); Lin et al (2009a); Bron and Kerbosch (2009) propose novel instantiated evolutionary solutions for clustering dynamic networks. Many other works (Sun et al 2007;Asur et al 2007;Yang et al 2009;Greene et al 2010;Tantipathananandh et al 2007;Duan et al 2009) study the evolutionary process of clusters in dynamic social network. Recently, the community evolution problem is studied (Lin et al 2009b;Tang et al 2008;Sun et al 2010) on heterogenous networks, in which there are more than one types of entities and relationships.…”

Section: Dynamic Clusteringmentioning

confidence: 99%

Incremental K-clique clustering in dynamic social networks

Duan

et al. 2011

Artif Intell Rev

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Clustering entities into dense parts is an important issue in social network analysis. Real social networks usually evolve over time and it remains a problem to efficiently cluster dynamic social networks. In this paper, a dynamic social network is modeled as an initial graph with an infinite change stream, called change stream model, which naturally eliminates the parameter setting problem of snapshot graph model. Based on the change stream model, the incremental version of a well known k-clique clustering problem is studied and incremental k-clique clustering algorithms are proposed based on local DFS (depth first search) forest updating technique. It is theoretically proved that the proposed algorithms outperform corresponding static ones and incremental spectral clustering algorithm in terms of time complexity. The practical performances of our algorithms are extensively evaluated and compared with the baseline algorithms on ENRON and DBLP datasets. Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.

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“…Palla et al [17] and Greene et al [18] developed models for tracking the evolution process of communities for dynamic networks, where each community is characterized by a series of significant evolutionary events, including growth, contraction, merging, splitting, birth and death. Asur et al [19] developed a framework for capturing and identifying community events which are used to characterize complex behavioral patterns of individuals and communities over time. Gauvin et al [20] used the non-negative tensor factorization method to extract the community activities of dynamic networks.…”

Section: Introductionmentioning

confidence: 99%

Evolution properties of the community members for dynamic networks

Yang

Guo

et al. 2017

Physics Letters A

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The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter ϕ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.

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An event-based framework for characterizing the evolutionary behavior of interaction graphs

Cited by 223 publications

References 34 publications

Social and place-focused communities in location-based online social networks

Social and place-focused communities in location-based online social networks

Incremental K-clique clustering in dynamic social networks

Evolution properties of the community members for dynamic networks

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