NPTEL Social Network Analysis Week 10 Assignment Answers 2024

1. Let G(V,E) be a graph where V represents the set of vertices and E represents the set of edges. If there is a mapping function defined as f : u → R^d, where u is an element of V, what is this mapping function called?

Random walk
Edge embedding
Graph embedding
Node embedding

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2. Which of the following is not a homogenous network?

Co-author network
Actor-Movie network
Citation network
Social media user network

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3. A complete graph embedding generally makes sense if we have more than one graph to embed.

True
False

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4. The number of features in a machine learning model is often smaller than the raw dataset.

True
False

Answer :-

5. What is the fundamental idea behind matrix factorization?

Increases the dimensionality of data
Structure-preserving dimensionality reduction
Data visualization
None of the above

Answer :-

6. Match the following graph representation of learning methods to their main category:

Dimensionality Reduction: HOPE, Matrix Factorization: DeepWalk, Random Walk: node2vec, Neural Network Based: GraRep
Dimensionality Reduction: node2vec, Matrix Factorization: Isometric Feature Mapping, Random Walk: GraRep, Neural Network Based: Kernel Methods
Dimensionality Reduction: Isometric Feature Mapping, Matrix Factorization: GraRep, Random Walk: node2vec, Neural Network Based: Graph Convolution Network
Dimensionality Reduction: node2vec, Matrix Factorization: Isometric Feature Mapping, Random Walk: GraRep, Neural Network Based: Graph Convolution Network

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7. How does the space complexity of representing a graph using its adjacency matrix change with an increase in the number of nodes?

increases linearly with the number of nodes
increases polynomially with the number of nodes
increases exponentially with the number of nodes
remains constant

Answer :-

8. What do the matrices U and V represent in the Singular Value Decomposition (SVD) of a matrix A=USVT?

U contains the right singular vectors, and V contains the left singular vectors.
Both U and V contain the same singular vectors.
U contains the left singular vectors, and V contains the right singular vectors.
U and V represent the original matrix A directly.

Answer :-

9. In GraRep, for a network G(V,E) and its adjacency matrix A. The transition probability of moving from node w to node c in exactly k-steps is given by: