scholarly journals Synwalk: community detection via random walk modelling

2022 ◽  
Author(s):  
Christian Toth ◽  
Denis Helic ◽  
Bernhard C. Geiger
Keyword(s):  
Random Walk ◽  
Random Walks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Future Research ◽  
Small Communities ◽  
Mixing Parameter ◽  
Modularity Maximization ◽  
Community Detection Algorithm ◽  
Empirical Networks

AbstractComplex systems, abstractly represented as networks, are ubiquitous in everyday life. Analyzing and understanding these systems requires, among others, tools for community detection. As no single best community detection algorithm can exist, robustness across a wide variety of problem settings is desirable. In this work, we present Synwalk, a random walk-based community detection method. Synwalk builds upon a solid theoretical basis and detects communities by synthesizing the random walk induced by the given network from a class of candidate random walks. We thoroughly validate the effectiveness of our approach on synthetic and empirical networks, respectively, and compare Synwalk’s performance with the performance of Infomap and Walktrap (also random walk-based), Louvain (based on modularity maximization) and stochastic block model inference. Our results indicate that Synwalk performs robustly on networks with varying mixing parameters and degree distributions. We outperform Infomap on networks with high mixing parameter, and Infomap and Walktrap on networks with many small communities and low average degree. Our work has a potential to inspire further development of community detection via synthesis of random walks and we provide concrete ideas for future research.

Integrating attributes of nodes solves the community structure partition effectively

2014 ◽  
Vol 28 (05) ◽  
pp. 1450037 ◽  
Author(s):  
Hui-Jia Li ◽  
Bingying Xu ◽  
Liang Zheng ◽  
Jia Yan
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Node Attribute ◽  
The Social ◽  
Modularity Maximization ◽  
Topology Information ◽  
Community Attribute ◽  
Community Detection Algorithm

Revealing ideal community structure efficiently is very important for scientists from many fields. However, it is difficult to infer an ideal community division structure by only analyzing the topology information due to the increment and complication of the social network. Recent research on community detection uncovers that its performance could be improved by incorporating the node attribute information. Along this direction, this paper improves the Blondel–Guillaume–Lambiotte (BGL) method, which is a fast algorithm based on modularity maximization, by integrating the community attribute entropy. To fulfill this goal, our algorithm minimizes the community attribute entropy by removing the boundary nodes which are generated in the modularity maximization at each iteration. By this way, the communities detected by our algorithm make a balance between modularity maximization and community attribute entropy minimization. In addition, another merit of our algorithm is that it is free of parameters. Comprehensive experiments have been conducted on both artificial and real networks to compare the proposed community detection algorithm with several state-of-the-art ones. As the experimental results indicate, our algorithm demonstrates superior performance.

scholarly journals A Comparative Analysis of Community Detection Algorithms on Artificial Networks

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Zhao Yang ◽  
René Algesheimer ◽  
Claudio J. Tessone
Keyword(s):  
Community Detection ◽  
Computing Time ◽  
Detection Algorithm ◽  
Network Size ◽  
Detection Algorithms ◽  
Mixing Parameter ◽  
Network Properties ◽  
Testing Algorithms ◽  
Community Detection Algorithm ◽  
Simple Network

Abstract Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time.

scholarly journals Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

2017 ◽  
Author(s):  
Javier O. Garcia ◽  
Arian Ashourvan ◽  
Sarah F. Muldoon ◽  
Jean M. Vettel ◽  
Danielle S. Bassett
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Human Cognition ◽  
Neural Function ◽  
Healthy Human ◽  
Detection Techniques ◽  
Statistical Measures ◽  
Neuroimaging Data ◽  
Modularity Maximization ◽  
Community Detection Algorithm

ABSTRACTThe human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can be used to identify communities or modules in brain graphs: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the utility of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

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