The Graph Modularity Maximization model is a mathematical algorithm used for graph clustering. It aims to partition a given graph into clusters such that the modularity of the graph is maximized. Modularity is a measure of how well the graph is divided into communities or modules, where a higher modularity indicates a stronger division into clusters.
The basic idea behind this model is to optimize the placement of nodes within clusters to maximize the modularity score. It achieves this by iteratively optimizing the assignment of nodes to clusters based on their connections or interactions within the graph. The model continues this process until no further improvement in modularity can be achieved.
Social Network Analysis: The Graph Modularity Maximization model can be used to identify communities or groups of individuals in a social network. It can help in understanding the structure and interactions among different social groups within the network.
Web Page Clustering: By applying the model to the link structure of web pages, it can determine related clusters of web pages. This can be used to improve search engine results by grouping similar pages together and presenting them as clusters.
Bioinformatics: The model can be applied to biological networks, such as protein-protein interaction networks or gene regulatory networks, to identify functional modules or communities. This can provide insights into the organization and functioning of biological systems.
Paper: "Fast unfolding of communities in large networks" by Vincent D. Blondel et al. - This paper introduces the Louvain method, which maximizes modularity and is widely used in graph clustering.
Paper: "Modularity and community structure in networks" by M. E. J. Newman - This paper explains the concept of modularity and its significance in community detection.
Python Library: NetworkX - NetworkX is a powerful Python library with various graph algorithms, including modularity maximization. It provides efficient tools for implementing and analyzing graph clustering using the modularity approach.
Here are five experts who have significant expertise in the field of graph modularity maximization:
Vincent D. Blondel: GitHub - Co-author of the Louvain method for community detection in large networks, which is based on modularity maximization.
M. E. J. Newman: GitHub - Author of influential research papers on network community structure and modularity optimization.
Andrea Lancichinetti: GitHub - Expert in network science and community detection, focusing on modularity maximization and other related algorithms.
Tiago P. Peixoto: GitHub - Researcher known for developing algorithms and tools for network analysis, including modularity maximization.
Aaron Clauset: GitHub - Expert in network science, specializing in community detection methods based on modularity maximization.