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

By Peter J. Mucha, Thomas Richardson, Kevin T. Macon, M. A. Porter, Jukka-Pekka Onnela. Science, 2009

2,081 citations recorded by the source corpus.

Network Notation Networks are often characterized by clusters of constituents that interact more closely with each other and have more connections to one another than they do with the rest of the components of the network. However, systematically identifying and studying such community structure in complicated networks is not easy, especially when the network interactions change over time or contain multiple types of connections, as seen in many biological regulatory networks or social networks. Mucha et al. (p. 876) developed a mathematical method to allow detection of communities that may be critical functional units of such networks. Application to real-world tasks—like making sense of the voting record in the U.S. Senate—demonstrated the promise of the method. A general mathematical method used to identify closely interacting groups can explain the behavior of complicated networks. Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/57c11e60f68f466516f96d5a3c4cef1bff94b7be

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In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Network neuroscience (2017, methodology): “This includes characterizing fluctuations in community structure of networks across time 59 , and implementing dynamic processes on networks as a diagnostic tool for explicitly linking micro-scale features of network organization to macro-scale characteristics of neurophysiological dynamics 60 .”
Random walks and diffusion on networks (2016, methodology): “ for multilayer modularity for these \multislice" networks, we generalize the RW interpretation of modularity for time-independent networks (see Section 5.3.1) to the case of multilayer networks [234]. Random walkers are allowed to move either between layers or within a layer. Consider a Poisso”
Ranking in interconnected multilayer networks reveals versatile nodes (2013, background): “It is important to discuss the difference between the topological structure which represents the core of this study, namely interconnected multilayer networks [4, 5, 6, 7, 8, 9, 10], and other multilayer structures which have been named multiplexes in the past and have been the subject of recent stu”
Describe, understand and predict: why do we need networks in ecology? (2016, background): “Excitingly, the newest tools and approaches from network science (Mucha et al. 2010; Kivel€a et al. 2014; Miele et al. 2014) are constantly being transferred to ecology (K efi”
Intrinsic and task-evoked network architectures of the human brain. (2014, positive): “To estimate both intrinsic and evoked architectures simultaneously, we used a tool (multislice community detection) developed to extract clusters and cluster changes in multinetwork systems (Mucha et al., 2010) and recently applied to neuroimaging data sets (Bassett et al.”
Community Discovery in Dynamic Networks (2017, methodology): “In (Mucha et al. , 2010) Political aﬃliation among sen-ators is studied based on vote similarities.”
DEMON: a local-first discovery method for overlapping communities (2012, methodology): “ve been written about modularity, either using it as a quality function to be optimized, or studying its properties and deciencies. One of the most advanced examples of modularity maximization CD is [17], where the authors use an extension of the modularity formula to cluster multiplex (evolving and”
Modern temporal network theory: a colloquium (2015, neutral): “Folino and Pizzuti [228] and the early paper by Mucha et al. [229] use an approach common in static networks – to maximize an objective function (a.k.a. modularity function ).”
Modular Brain Networks (2015, positive): “An alternative approach for finding communities in multislice networks (Mucha et al. 2010) consists of introducing a coupling parameter that links corresponding nodes across slices and then using a quality function that extends Q ( γ ) multislice networks.”
A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System (2018, neutral): “networks [35]–[37].”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “According to this generalized framework each layer models the interactions of the system at the time t, and the extracted time-varying graph metrics quantify the evolution of the topological properties across time [176, 177].”
The multilayer nature of ecological networks (2015, background): “Also, if each layer is treated individually we assume layers are independent, which can lead to qualitatively incorrect conclusions and the inability to study questions about time-dependent phenomena [31,32,65]). Number of species Module ID Year Figure 1. Modules across six years in a host{parasite ”
Measuring Large-Scale Social Networks with High Resolution (2014, background): “While recent work [114,115] begin to explore the topic, a coherent theory describing multiplex, weighted, and directed networks remains beyond the frontier of our current understanding.”