Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm

Parés, Ferran; Gasulla, Dario Garcia; Vilalta, Armand; Moreno, J. L.; Ayguadé, Eduard; Labarta, Jesús; Cortés, Ulises; Suzumura, Toyotaro

doi:10.1007/978-3-319-72150-7_19

Cited by 82 publications

(83 citation statements)

References 18 publications

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“…One may notice some similarities between the problem of finding communities in unsigned networks [26][27][28][29][30][31] and that of partitioning signed networks to minimize the frustration count. One key difference is that in the latter problem for every pair of vertices there are three cases (as opposed to two): a positive edge, a negative edge, or no edge between the two vertices.…”

Section: Evaluating Balance and Frustrationmentioning

confidence: 99%

Detecting coalitions by optimally partitioning signed networks of political collaboration

Aref

Neal

2020

Sci Rep

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We propose new mathematical programming models for optimal partitioning of a signed graph into cohesive groups. To demonstrate the approach's utility, we apply it to identify coalitions in US Congress since 1979 and examine the impact of polarized coalitions on the effectiveness of passing bills. Our models produce a globally optimal solution to the NP-hard problem of minimizing the total number of intra-group negative and inter-group positive edges. We tackle the intensive computations of dense signed networks by providing upper and lower bounds, then solving an optimization model which closes the gap between the two bounds and returns the optimal partitioning of vertices. our substantive findings suggest that the dominance of an ideologically homogeneous coalition (i.e. partisan polarization) can be a protective factor that enhances legislative effectiveness.

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Section: Evaluating Balance and Frustrationmentioning

confidence: 99%

Detecting coalitions by optimally partitioning signed networks of political collaboration

Aref

Neal

2020

Sci Rep

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“…Fluid this model was introduced in Parés et al (2017) and is based on the simple idea of fluids (i.e., communities) interacting in an environment (i.e., a non-complete graph), expanding and contracting. It is a propagation-based algorithm and it allows to specify the number of desired communities (k) and it is asynchronous, where each vertex update is computed using the latest partial state of the graph.…”

Section: Crisp Communitiesmentioning

confidence: 99%

CDLIB: a python library to extract, compare and evaluate communities from complex networks

2019

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Community Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library -namely CDLIB -designed to serve this need. The aim of CDLIB is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.

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“…FluidC. FluidC is probably the first community detection algorithm based on the idea of fluid dyeing [35]. This algorithm needs to determine the number of communities in advance.…”

Section: Comparing Algorithmsmentioning

confidence: 99%

Community Detection Based on a Preferential Decision Model

et al. 2020

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The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

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Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm

Cited by 82 publications

References 18 publications

Detecting coalitions by optimally partitioning signed networks of political collaboration

Detecting coalitions by optimally partitioning signed networks of political collaboration

CDLIB: a python library to extract, compare and evaluate communities from complex networks

Community Detection Based on a Preferential Decision Model

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