| Course Name | Social Network Analytics |
| Course Code | 24ASD644 |
| Program | M.Sc. in Applied Statistics and Data Analytics |
| Credits | 3 |
| Campus | Coimbatore , Kochi |
Introduction: Applications, Preliminaries, Three Levels of SNA, Graph Visualization Tools.
Network Measures: Network Basics, Node Centrality, Assortativity, Transitive and Reciprocity, Similarity.
Network Growth Models: Properties of real-world networks, Random network model. Preferential Attachment model, Price’s model – Local-world network growth model.
Link Analysis: Applications, Strong and weak ties – Link analysis and algorithms, Page Rank.
Community Structure in Networks: Applications, Types of Community detection methods, Disjoint community detection, overlapping community detection, local community detection.
Cascade Behavior and Network Effects: Cascade model – probabilistic cascade – Cascade prediction.
Anomaly Detection in Static Networks: Outlier vs Network-based anomalies, challenges in anomaly detection.
Graph Representation Learning: Machine Learning pipelines, Intuition behind representational learning – benefits – representational learning methods.
Case Study: Analysis of social network datasets, Modeling the spread of COVID 19, Recommendation System.
Course Outcome:
|
CO1: |
Understand the levels of SNA and network measures |
|
CO2: |
Familiarize with network growth models |
|
CO3: |
Gain knowledge about different community structures and link prediction models |
|
CO4: |
Understand cascade behavior in networks |
|
CO5: |
Predict and recommend in online social networks |
CO-PO Mapping:
|
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
|
|
CO1 |
2 |
2 |
2 |
2 |
2 |
2 |
– |
– |
– |
– |
1 |
1 |
|
CO2 |
3 |
3 |
2 |
2 |
2 |
2 |
– |
– |
– |
– |
1 |
1 |
|
CO3 |
2 |
2 |
3 |
2 |
2 |
2 |
– |
– |
– |
– |
1 |
1 |
|
CO4 |
3 |
3 |
3 |
2 |
2 |
2 |
– |
– |
– |
– |
1 |
1 |
Text Books/ Reference Books:
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