This course introduces the fundamentals of Social Network Analysis (SNA), including graph theory, network structures, and social media analytics. It covers key concepts such as centrality measures, community detection, link prediction, and network visualization. Students will learn to analyze real-world social networks and apply SNA techniques to solve practical problems.

Course Prerequisites: Data Structures and Algorithms

Course Objectives: (This course  will enable students to)

• To introduce the fundamentals of Social Network Analysis and its significance in understanding societal connections and behaviors.

• To analyze various models of network growth and understand the properties of real-world networks.

• To explore link analysis algorithms and their applications in understanding relationships within a network.

• To study community detection methods and their relevance in identifying meaningful clusters within networks.

• To understand link prediction techniques and their application in forecasting future connections within a network.

Course Outcomes: (The student will be able to)

CO1 : Illustrate the core concepts of Social Network Analysis and its levels of study.  L2

CO2 : Demonstrate the different network growth models for real-world networks. L2

CO3 : Apply algorithms of PageRank and SimRank to analyze and interpret link relationships. L3

CO4 : Apply community detection methods and evaluate their effectiveness in real-world scenarios. L3

CO5 : Analyze heuristic, probabilistic, and supervised models to predict network link formations and changes. L3