💡[Feature]: adding 'Social Network Analysis' in Data Analysis including Readme and dataset

[vermu490]

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Feature Description

The proposed feature introduces a Social Network Analysis (SNA) model that leverages relational data to interpret, analyze, and visualize interactions among users, groups, or entities within a network. Unlike traditional methods, which often rely on hierarchical clustering with fixed assumptions, this approach adopts a hierarchical, non-parametric model that dynamically detects and visualizes overlapping clusters within social networks.

With the massive amount of information generated by social networks every day, organizing and understanding this data is crucial. Traditional methods typically involve non-overlapping clustering or predefined assumptions, limiting their flexibility and scope. In contrast, our model introduces several enhancements:

• Automatic Social Circle Discovery: The model identifies social circles within an ego-network without requiring predefined parameters, making it adaptable to diverse network sizes and structures.
• Incorporation of Structural and User-Attribute Information: By leveraging the principle of homophily, which assumes users with similar attributes form closer ties, this feature identifies more cohesive and realistic social circles.
• Overcoming Limitations of Traditional Hierarchical Clustering: Unlike traditional hierarchical methods, which often fall short in overlapping or dynamic contexts, our model is designed to recognize and represent overlapping clusters effectively.

Use Case

A specific use case for this feature is in analyzing ego-networks on platforms like Facebook or LinkedIn, where users belong to multiple social circles that naturally overlap (e.g., family, friends, colleagues). By identifying these overlapping circles without predefined constraints, this feature allows users and researchers to uncover deeper insights into the ways relationships intersect and evolve.

Benefits

This feature would bring several key benefits to the project and the broader community:

1. Enhanced Understanding of Social Networks: By uncovering overlapping clusters, this feature enables richer, more nuanced insights into social dynamics and relationship structures.
2. Adaptability and Scalability: The non-parametric nature allows this model to be applied across various network sizes and contexts without modification.
3. Empirical Validation: Our testing on Facebook datasets of ego-networks indicates that the model performs comparably to benchmark results, underscoring its accuracy and robustness in real-world applications.

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Priority

High

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• [X] I have read the Contributing Guidelines
• [X] I'm a GSSOC'24 contributor
• [X] I want to work on this issue

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