Social Network Analysis

 

1: Introduction & Tools

1. Define social network analysis and explain its importance in real-world systems.

2. Describe the basic components of a graph. How are these used in network modeling?

3. Explain the differences between directed and undirected networks with suitable examples.

4. Write a Python snippet using NetworkX to create and visualize a simple graph.

5. Describe the main features and uses of Google Colab for social network analysis tasks.

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 2: Network Measures

1. Define degree centrality and closeness centrality. How do they differ in interpretation?

2. What is betweenness centrality? Explain its significance in identifying bridge nodes.

3. Describe clustering coefficient. What does a high clustering coefficient imply?

4. Explain the concept of average path length in a network.

5. Write a Python code using NetworkX to compute degree centrality and plot it.

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3: Network Growth Models

1. Compare and contrast the Erdős–Rényi model and Barabási–Albert model.

2. Explain the concept of preferential attachment in network evolution.

3. How do random graphs help in understanding real-world network structures?

4. What are small-world networks? Explain with the help of the Watts–Strogatz model.

5. Describe the steps to generate a scale-free network using NetworkX.

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 4: Link Analysis

1. Describe the PageRank algorithm and its applications.

2. What is the HITS algorithm? How does it differ from PageRank?

3. Explain the concepts of hub and authority scores.

4. How does link analysis help in ranking web pages?

5. Illustrate with code how to compute PageRank of a graph in NetworkX.

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 5: Graph Visualization & Community Detection I

1. Why is graph visualization important in network analysis?

2. Discuss different layout algorithms available for visualizing graphs.

3. What are communities in a network? Give real-world examples.

4. Explain the Girvan–Newman algorithm for community detection.

5. Demonstrate a visualization of communities using NetworkX and matplotlib.

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 6: Community Detection II

1. What is modularity? How does it help in evaluating community structures?

2. Explain the Louvain method for community detection.

3. Compare the Girvan–Newman and Louvain algorithms in terms of accuracy and efficiency.

4. How can overlapping communities be identified in a network?

5. Write code to detect communities in a graph and calculate modularity using NetworkX.

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7: Link Prediction

1. What is link prediction and why is it important in social networks?

2. Describe common similarity-based methods for link prediction.

3. Explain how machine learning can be applied to the link prediction problem.

4. Describe and compare Jaccard Coefficient and Adamic-Adar Index.

5. Implement a simple link prediction model using NetworkX.

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 8: Cascade Behavior and Network Effects

1. Explain the concept of information cascade with an example.

2. How does the threshold model of behavior adoption work in networks?

3. What are network externalities? How do they affect user behavior?

4. Describe diffusion models used in analyzing cascade behaviors.

5. Simulate a simple cascade process using a Python script.

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9: Anomaly Detection

1. What constitutes an anomaly in a network?

2. Explain how network topology can help detect anomalous nodes or links.

3. Discuss different types of anomalies (e.g., structural, temporal).

4. Describe a method to detect fraud in e-commerce networks.

5. Write a Python function to identify nodes with unusual degree distributions.

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10: Intro to Deep Learning & Graph Representation I

1. What are the challenges in applying deep learning to graphs?

2. Define graph embeddings and explain their purpose.

3. Describe the concept of node2vec and how it captures network features.

4. How does graph structure differ from regular data in DL applications?

5. Illustrate how to convert a graph into a format usable by deep learning models.

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11: Graph Representation II

1. Explain Graph Convolutional Networks (GCNs) and their working principles.

2. How is information propagated in GCNs?

3. Describe one practical use case of Graph Neural Networks.

4. What are the limitations of shallow embedding methods like DeepWalk?

5. Implement a basic graph representation learning model using node2vec.

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12: Applications & Case Studies

1. Describe a real-world application where social network analysis improved outcomes.

2. How can network science help in analyzing fake news spread?

3. Discuss a case study involving anomaly detection in financial transaction networks.

4. Explain the role of SNA in recommendation systems.

5. Summarize key learnings from the course and their practical significance.

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