Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

Mucha, Peter J.; Richardson, Thomas; Macon, K. T.; Porter, Mason A.; Onnela, Jukka Pekka

doi:10.1126/science.1184819

Cited by 1,878 publications

(1,830 citation statements)

References 26 publications

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“…The condition to have an increase of q( ) expressed by equation (9) can be also written in terms of the Jensen-Shannon divergence of the layers to be aggregated. For the sake of simplicity, let us assume that the two layers C [1] and C [2] aggregated to obtain the new configuration C 0 have the same number of links.…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, in biological systems basic constituents such as proteins can have physical, co-localization, genetic or many other types of interactions. Recently, it has been shown that retaining such multi-dimensional information 7 and modelling the structure of interdependent and multilayer systems respectively through interdependent 8 and multilayer networks [9][10][11][12] reveals new nontrivial structural properties [13][14][15][16][17][18][19][20] and relevant emergent physical phenomena [21][22][23][24][25][26][27] .…”

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confidence: 99%

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Structural reducibility of multilayer networks

et al. 2015

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Many complex systems can be represented as networks consisting of distinct types of interactions, which can be categorized as links belonging to different layers. For example, a good description of the full protein-protein interactome requires, for some organisms, up to seven distinct network layers, accounting for different genetic and physical interactions, each containing thousands of protein-protein relationships. A fundamental open question is then how many layers are indeed necessary to accurately represent the structure of a multilayered complex system. Here we introduce a method based on quantum theory to reduce the number of layers to a minimum while maximizing the distinguishability between the multilayer network and the corresponding aggregated graph. We validate our approach on synthetic benchmarks and we show that the number of informative layers in some real multilayer networks of protein-genetic interactions, social, economical and transportation systems can be reduced by up to 75%.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Structural reducibility of multilayer networks

et al. 2015

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“…The works incorporating a time dimension into the community detection (like Refs. [110,118,132,133]) operate on aggregated timeslices of the temporal network. One can imagine clustering algorithms based on more elaborate temporal structures, like time-respecting paths (a rare exception is Ref.…”

Section: Future Outlookmentioning

confidence: 99%

Temporal networks

2012

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A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names

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“…Multilayer networks can also be used to model dynamic networks. In [19] authors introduced a multi-slice generalization of modularity measure to quantify the quality of the detected community structure in a multiplex network. Apart from being dynamic, another significant characteristic of real-world social networks is the presence of leaders, i.e.…”

Section: Introductionmentioning

confidence: 99%

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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Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

Cited by 1,878 publications

References 26 publications

Structural reducibility of multilayer networks

Structural reducibility of multilayer networks

Temporal networks

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

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