Identification of overlapping community structure in complex networks using fuzzy c-means clustering
نویسندگان
چکیده
Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, we devise a novel algorithm to identify overlapping communities in complex networks by the combination of a new modularity function based on generalizing NG’s Q function, an approximation mapping of network nodes into Euclidean space and fuzzy c-means clustering. Experimental results indicate that the new algorithm is efficient at detecting both good clusterings and the appropriate number of clusters. r 2006 Elsevier B.V. All rights reserved.
منابع مشابه
Detecting Overlapping Communities in Social Networks using Deep Learning
In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...
متن کاملDetecting Community Structure in Complex Networks Using Bacterial Chemotaxis with Fuzzy C-means Clustering
Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, the bacterial chemotaxis (BC) strategy is used to maximize the modularity of a network, associating with a dissimilarity-index-based and with a diffusion-distance-based fuzzy c-means clustering iterative procedure. The proposed algorithm outperforms ...
متن کاملAn Algorithm for Detecting Community Structure of Complex Networks based on Clustering
There are considerable interest in algorithms for detecting community structure, which is fundamental for analyzing the relationship between structure and function in complex networks. In this paper, after the introduction of some traditional approaches for detecting community structure and data mining clustering algorithms, we propose Mapping Vertex into Vector(MVV) algorithm, which can conver...
متن کاملBilateral Weighted Fuzzy C-Means Clustering
Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...
متن کاملOutlier Detection Using Extreme Learning Machines Based on Quantum Fuzzy C-Means
One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structur...
متن کامل