FAST Social Network Project

Executive Summary

The FAST Social Network project aimed to design and implement a C++ program modeling and analyzing a social network within the FAST-NUCES university setting. Using graph algorithms, the focus was on representing relationships between students, faculty, and staff to gain insights into the social structure. The team comprised Muhammad Kashif, Bilal Saleem, Usman Nazeer, and Muhammad Huzaifa, each contributing expertise in different aspects of graph algorithms and analysis.

Project Contributions

1. Usman:

   • Graph representation
   • Breadth-First Search (BFS)
   • Topological Sort
   • Highest Degree Centrality
   • Academic Collaborations

2. Kashif:

   • Adjacency Matrix
   • Prim's algorithm
   • Influential Individuals
   • Community Detection

3. Bilal:

   • Depth-First Search (DFS)
   • Event Attendance Analysis
   • Dynamic Programming

4. Huzaifa:

   • Find Interdisciplinary Collaborations
   • Kruskal's algorithm
   • Dijkstra's algorithm

5. Collective Efforts:

   • Relationship Analysis
   • Communication Patterns
   • Collaboration Networks
   • Project Report

Project Implementation Overview

1. Graph Representation: The team designed a Graph class to represent the university social network, defining nodes for individuals and edges for relationships.

2. Relationship Analysis: Implemented functionality for adding individuals, establishing relationships, and visualizing the network graph.

3. Communication Patterns: Analyzed communication patterns to identify individuals with high communication centrality and groups with strong ties.

4. Collaboration Networks: Developed algorithms to identify academic and interdisciplinary collaboration networks.

5. Influence Analysis: Determined influential individuals based on academic impact, leadership roles, and event participation.

6. Community Detection: Implemented a community detection algorithm to identify social clusters within the university.

7. Event Attendance Analysis: Analyzed attendance patterns at university events to identify popular events and frequent participants.

8. Dynamic Network: Designed the system to accommodate dynamic changes in the social network.

Insights and Challenges

The project revealed insights into communication patterns, collaboration networks, and influential individuals within the university. Challenges included balancing algorithmic efficiency with real-world complexities, especially in handling dynamic network changes.

Future Enhancements

Future enhancements may involve refining algorithms for better performance, incorporating additional influence analysis metrics, and expanding dynamic network capabilities.

Additional Analyses:

• Algorithm Performance Analysis
• Time Complexity Analysis
• Dataset Descriptions

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Note: For detailed information, refer to the provided PDF document.