A review and comparative analysis of coarsening algorithms on bipartite networks

Valejo, Alan; Santos, Wellington de Oliveira dos; Naldi, Murilo Coelho; Zhao, Liang

doi:10.1140/epjs/s11734-021-00159-0

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…This is a problem that deserves further investigation. Interestingly, the impact of distinct coarsening policies on the effectiveness of the multilevel strategy applied in different data mining tasks is also a relevant research topic where interactive visualization itself can play an important role (Valejo et al, 2021 ).…”

Section: Multilevel Methods On Bipartite Networkmentioning

confidence: 99%

Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks

Valejo

Fabbri

Lopes

et al. 2022

Front. Res. Metr. Anal.

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Bipartite networks are pervasive in modeling real-world phenomena and play a fundamental role in graph theory. Interactive exploratory visualization of such networks is an important problem, and particularly challenging when handling large networks. In this paper we present results from an investigation on using a general multilevel method for this purpose. Multilevel methods on networks have been introduced as a general approach to increase scalability of community detection and other complex optimization algorithms. They employ graph coarsening algorithms to create a hierarchy of increasingly coarser (reduced) approximations of an original network. Multilevel coarsening has been applied, e.g., to the problem of drawing simple (“unipartite”) networks. We build on previous work that extended multilevel coarsening to bipartite graphs to propose a visualization interface that uses multilevel coarsening to compute a multi-resolution hierarchical representation of an input bipartite network. From this hierarchy, interactive node-link drawings are displayed following a genuine route of the “overview first, zoom and filter, details on demand” visual information seeking mantra. Analysts may depart from the coarsest representation and select nodes or sub-graphs to be expanded and shown at greater detail. Besides intuitive navigation of large-scale networks, this solution affords great flexibility, as users are free to select different coarsening strategies in different scenarios. We illustrate its potential with case studies involving real networks on distinct domains. The experimental analysis shows our strategy is effective to reveal topological structures, such as communities and holes, that may remain hidden in a conventional node-link layout. It is also useful to highlight connectivity patterns across the bipartite layers, as illustrated in an example that emphasizes the correlation between diseases and genes in genetic disorders, and in a study of a scientific collaboration network of authors and papers.

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Section: Multilevel Methods On Bipartite Networkmentioning

confidence: 99%

Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks

Valejo

Fabbri

Lopes

et al. 2022

Front. Res. Metr. Anal.

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“…Valejo et al [9] provide a review and comparative analysis of coarsening algorithms on bipartite networks. They present illustrative examples of how problems in bipartite networks can be addressed using these coarsening algorithms.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…We subdivide the contributions into three main fields: theory and methods, applications to neuroscience, and Earth science applications. The theoretical aspects considered here cover a broad spectrum of topics such as collective dynamics of excitable systems, cluster dynamics [1], interplay of noise and feedback [2], subdiffusive behavior [5], spreading phenomena on networks [8], coarsening [9], multipartite networks, partial synchrony [11], and time-delayed interactions [13,14]. In a series of works, complex networks are successfully employed as tools for detecting artificially inserted data [3], community detection [4,12], identification of time series patterns [6], and modeling a voter dynamics [15].…”

Section: Introductionmentioning

confidence: 99%

Dynamical phenomena in complex networks: fundamentals and applications

Yanchuk

Roque

Macau

et al. 2021

Eur. Phys. J. Spec. Top.

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This special issue presents a series of 33 contributions in the area of dynamical networks and their applications. Part of the contributions is devoted to theoretical and methodological aspects of dynamical networks, such as collective dynamics of excitable systems, spreading processes, coarsening, synchronization, delayed interactions, and others. A particular focus is placed on applications to neuroscience and Earth science, especially functional climate networks. Among the highlights, various methods for dealing with noise and stochastic processes in neuroscience are presented. A method for constructing weighted networks with arbitrary topologies from a single dynamical node with delayed feedback is introduced. Also, a generalization of the concept of geodesic distances, a path-integral formulation of network-based measures is developed, which provides fundamental insights into the dynamics of disease transmission. The contributions from the Earth science application field substantiate predictive power of climate networks to study challenging Earth processes and phenomena.

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“…It is also referred to as an affiliation or two-mode network (Kevork and Kauermann 2022 ). The heterogeneous nature of the bipartite network makes it a realistic model of the real-world system and applicable across a wide range of research fields, particularly in the studies related to science and technology (Valejo et al 2021 ). It is commented as capable of providing insightful representation from mutualistic networks in ecology to trade networks in the economy (Saracco et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A methodology framework for bipartite network modeling

et al. 2023

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The graph-theoretic based studies employing bipartite network approach mostly focus on surveying the statistical properties of the structure and behavior of the network systems under the domain of complex network analysis. They aim to provide the big-picture-view insights of a networked system by looking into the dynamic interaction and relationship among the vertices. Nonetheless, incorporating the features of individual vertex and capturing the dynamic interaction of the heterogeneous local rules governing each of them in the studies is lacking. The methodology in achieving this could hardly be found. Consequently, this study intends to propose a methodology framework that considers the influence of heterogeneous features of each node to the overall network behavior in modeling real-world bipartite network system. The proposed framework consists of three main stages with principal processes detailed in each stage, and three libraries of techniques to guide the modeling activities. It is iterative and process-oriented in nature and allows future network expansion. Two case studies from the domain of communicable disease in epidemiology and habitat suitability in ecology employing this framework are also presented. The results obtained suggest that the methodology could serve as a generic framework in advancing the current state of the art of bipartite network approach. Graphical Abstract

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A review and comparative analysis of coarsening algorithms on bipartite networks

Cited by 8 publications

References 23 publications

Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks

Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks

Dynamical phenomena in complex networks: fundamentals and applications

A methodology framework for bipartite network modeling

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