I will do community detection in social network analysis

Introducing community detection services within social network analysis showcases the expertise of a current Ph.D. candidate with five years of experience in this area. The individual's work covers various aspects such as community detection, centrality measures, link prediction, motif identification, team formation, shortest path analysis, influential nodes, and network visualization, to name a few. They have a comprehensive understanding of both local and global network features and work with a range of networks, including directed, undirected, and weighted ones. Their skill set includes proficiency with tools and platforms like R, Igraph, NetworkX, Gephi, NetMiner, UCINET, Pajek, and Python.

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Introducing community detection services in social network analysis

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I am currently pursuing a Ph.D. specializing in social network analysis and bring with me five years of extensive experience in this dynamic field. My work spans a diverse range of topics, including Community Detection, Centrality Measures, Link Prediction, Motif Identification, Team Formation, Shortest Path Analysis, Influential Nodes, Network Visualization, Power Law Distribution, Degree Distribution, Network Sampling, Random Graphs, and analyzing Weakly and Strongly Connected Components, among other local and global network features. Additionally, I have experience working with both directed and undirected, as well as weighted networks. My expertise extends to a variety of tools and platforms essential for social network analysis, including R, Igraph, NetworkX, Gephi, NetMiner, UCINET, Pajek, and Python.

Here's What I Offer:

Network-Level Analysis: Diameter, Density, Clustering Coefficient, Betweenness, Closeness, Structural Properties, Triangles, Shortest Paths, and more.
Node-Level Analysis: Degree, Hops, Neighbors, Nodes, and Edges.
Comprehensive Social Network Analysis and Visualization.

Why Choose Me?

Published over eight research articles in the field of social network analysis.
Developed a new centrality measure known as "SAM Centrality."
Introduced a novel similarity measure, "SAM Similarity."