What is it about?
Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of a brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks.
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Why is it important?
Analysis of various community detection algorithms, datasets used by graphs, and traditional relational data clustering for the experimental study and its measures to evaluate clustering algorithms are discussed. Future scope and research opportunities relevant to community detection in complex networks are described.
Perspectives
Objective - As a common data structure, the graph has become popular nowadays for Big Data Analytics. For such graph structure, it is essential to model appropriate structures, and their interactions, which can apply to various applications — for these applications, modeling, and generating a graph based on their structure, attributes, weight, and direction (whether directed or undirected) have become important tasks. Methods- We discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them.
Dr Smita Agrawal
Nirma University of Science and Technology
Read the Original
This page is a summary of: Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review, Recent Patents on Computer Science, July 2019, Bentham Science Publishers,
DOI: 10.2174/2213275912666190710183635.
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