A Novel Similarity-Based Modularity Function for Graph Partitioning

Feng, Zhidan; Xu, Xiaowei; Yuruk, Nurcan; Schweiger, Thomas A. J.

doi:10.1007/978-3-540-74553-2_36

Cited by 46 publications

(22 citation statements)

References 14 publications

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“…Feng et al [3] proposed a genetic algorithm for network clustering. The algorithm operates on a weighted graph that represents similarities between network users.…”

Section: Related Workmentioning

confidence: 99%

Agglomerative genetic algorithm for clustering in social networks

Lipczak

Milios

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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Size and complexity of data repositories collaboratively created by Web users generate a need for new processing approaches. In this paper, we study the problem of detection of fine-grained communities of users in social networks, which can be defined as clustering with a large number of clusters. The practical size of social networks makes the traditional evolutionary based clustering approaches, which represent the entire clustering solution as one individual, hard to apply. We propose an Agglomerative Clustering Genetic Algorithm (ACGA): a population of clusters evolves from the initial state in which each cluster represents one user to a high quality clustering solution. Each step of the evolutionary process is performed locally, engaging only a small part of the social network limited to two clusters and their direct neighborhood. This makes the algorithm practically useful independently of the size of the network. Evaluation on two social network models indicates that ACGA is potentially able to detect communities with accuracy comparable or better than two typical centralized clustering algorithms even though ACGA works under much stricter conditions.

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“…Feng et al [3] proposed a genetic algorithm for network clustering. The algorithm operates on a weighted graph that represents similarities between network users.…”

Section: Related Workmentioning

confidence: 99%

Agglomerative genetic algorithm for clustering in social networks

Lipczak

Milios

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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“…The modularity measure can be utilized for clustering validity checking. Here, we adopt a similarity-based modularity function to measure the quality of the detected communities [24,25]. It is defined as…”

Section: Core Areamentioning

confidence: 99%

“…The modularity measure is a benefit function that measures the quality of a division of a network into groups or communities [24,25]. It is defined in Equal 11.…”

Section: Criterion For Accuracy Evaluationmentioning

confidence: 99%

IncOrder: Incremental density-based community detection in dynamic networks

Huang

Zhang

Liu

et al. 2014

Knowledge-Based Systems

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“…Modularity, however, can measure the quality of a partitioning into multiple clusters, and thus, it is more suitable for our problem. Instead of the original modularity that uses the number of edges or node degrees in each cluster, we adopt the extended modularity [9] that uses the structure similarity, which has been known to be more effective than the original modularity. The extended modularity Q s is defined as follows:…”

Section: Clustering Of Optimal Modularitymentioning

confidence: 99%

A particle-and-density based evolutionary clustering method for dynamic networks

2009

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Recently, dynamic networks are attracting increasing interest due to their high potential in capturing natural and social phenomena over time. Discovery of evolutionary communities in dynamic networks has become a critical task. The previous evolutionary clustering methods usually adopt the temporal smoothness framework, which has a desirable feature of controlling the balance between temporal noise and true concept drift of communities. They, however, have some major drawbacks: (1) assuming only a fixed number of communities over time; and (2) not allowing arbitrary start/stop of community over time. The forming of new communities and dissolving of existing communities are very common phenomena in real dynamic networks. In this paper, we propose a new particle-and-density based evolutionary clustering method that efficiently discovers a variable number of communities of arbitrary forming and dissolving. We first model a dynamic network as a collection of lots of particles called nano-communities, and a community as a densely connected subset of particles, called a quasi l-clique-by-clique (shortly, l-KK ). Each particle contains a small amount of information about the evolution of data or patterns, and the quasi l-KK s inherent in a given dynamic network provide us with guidance on how to find a variable number of communities of arbitrary forming and dissolving. We propose a density-based clustering method that efficiently finds temporally smoothed local clusters of high quality by using a cost embedding technique and optimal modularity. We also propose a mapping method based on information theory that makes sequences of smoothed local clusters as close as possible to data-inherent quasi l-KKs. The result of the mapping method allows us to easily identify the stage of each community among the three stages: evolving, forming, and dissolving. Experimental studies, by using various data sets, demonstrate that our method improves the clustering accuracy, and at the same time, the time performance by an order of magnitude compared with the current state-of-the art method.

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A Novel Similarity-Based Modularity Function for Graph Partitioning

Cited by 46 publications

References 14 publications

Agglomerative genetic algorithm for clustering in social networks

Agglomerative genetic algorithm for clustering in social networks

IncOrder: Incremental density-based community detection in dynamic networks

A particle-and-density based evolutionary clustering method for dynamic networks

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