Cohesive Co-evolution Patterns in Dynamic Attributed Graphs

Desmier, Elise; Plantevit, Marc; Robardet, Céline; Boulicaut, Jean-François

doi:10.1007/978-3-642-33492-4_11

Cited by 38 publications

(43 citation statements)

References 25 publications

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“…We selected the dynamic co-authorship network from [25], extracted from the DBLP database. Each one of the 2145 nodes represents an author.…”

Section: Resultsmentioning

confidence: 99%

A method for characterizing communities in dynamic attributed complex networks

Orman¹,

Labatut²,

Plantevit³

et al. 2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

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Abstract-Many methods have beenproposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. In its simplest form, a community structure takes the form of apartition of the node set. From the modeling point of view, to be of some utility, this partition must then be characterized relatively to the properties of the studied system. However, if most of the existing works focus on defining methods for the detection of communities, only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either in the type of data they handle, or by the nature of the results they output. In this work, we propose a method to efficiently support such a characterization task. We first define a sequence-based representation of networks, combining temporal information, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We also show how to detect unusual behavior in a community, and highlight outliers. Finally, as an illustration, we apply our method to a network of scientific collaborations.

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“…We selected the dynamic co-authorship network from [25], extracted from the DBLP database. Each one of the 2145 nodes represents an author.…”

Section: Resultsmentioning

confidence: 99%

A method for characterizing communities in dynamic attributed complex networks

Orman¹,

Labatut²,

Plantevit³

et al. 2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

Self Cite

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“…We selected the dynamic co-authorship network of Desmier et al (2012), extracted from the DBLP database. Each one of the 2145 nodes represents an author.…”

Section: Data and Preprocessingmentioning

confidence: 99%

Interpreting communities based on the evolution of a dynamic attributed network

Orman

Labatut²,

Plantevit³

et al. 2015

Soc. Netw. Anal. Min.

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Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed, or even dynamic complex networks. From the modeling point of view, to be of some utility, the community structure must be characterized relatively to the properties of the studied system. However, most of the existing works focus on the detection of communities, and only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either by the type of data they handle or by the nature of the results they output. In this work, we see the interpretation of communities as a problem independent from the detection process, consisting in identifying the most characteristic features of communities. We give a formal definition of this problem and propose a method to solve it. To this aim, we first define a sequence-based representation of networks, combining temporal information, community structure, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset and use them to characterize the communities. We study the performance of our method on artificially generated dynamic attributed networks. We also empirically validate our framework on real-world systems: a DBLP network of scientific collaborations, and a LastFM network of social and musical interactions.

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“…They extract groups of connected vertices whose vertex weights follow a similar evolution, increasing or decreasing, on consecutive time stamps. Desmier et al [6] discover neighborhood similar set of vertices whose attributes follow the same trends. All the above works only assess the interest of the patterns by means of frequency-based constraints.…”

Section: Related Workmentioning

confidence: 99%

“…The second problem was recently tackled in [6], where an algorithm that mines cohesive co-evolution patterns is proposed. These patterns identify sets of vertices that are similar from the point of view of their attribute values and of the vertices in their neighborhood.…”

Section: Introductionmentioning

confidence: 99%

Trend Mining in Dynamic Attributed Graphs

Desmier

Plantevit

Robardet

et al. 2013

Advanced Information Systems Engineering

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Abstract.Many applications see huge demands of discovering important patterns in dynamic attributed graph. In this paper, we introduce the problem of discovering trend sub-graphs in dynamic attributed graphs. This new kind of pattern relies on the graph structure and the temporal evolution of the attribute values. Several interestingness measures are introduced to focus on the most relevant patterns with regard to the graph structure, the vertex attributes, and the time. We design an efficient algorithm that benefits from various constraint properties and provide an extensive empirical study from several real-world dynamic attributed graphs.

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Cohesive Co-evolution Patterns in Dynamic Attributed Graphs

Cited by 38 publications

References 25 publications

A method for characterizing communities in dynamic attributed complex networks

A method for characterizing communities in dynamic attributed complex networks

Interpreting communities based on the evolution of a dynamic attributed network

Trend Mining in Dynamic Attributed Graphs

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