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6 questions of 5 marks each, 10 multiple choice and 10 fillup the blanks and match the following.
Unit 1 questions
1. Define:(a) social media mining, social media (as per Kaplan and Haenlein) 2m
(b)Mention 2 key characteristics of social media that differentiate it
from other media? 1m
(c.)Enumerate the 4 novel challenges encountered in social media mining 2m
2.Give proof for any one of the following
(a)The summation of degrees in an undirected graph is twice the number of edges
(b)In an undirected graph, there are an even number of nodes having odd degree.
(c)In any directed graph, the summation of in-degrees is equal to the summation of out-degrees.
3.(a)Draw a 6-node graph and represent its adjacency matrix
(b)Enumerate graph representation approaches.
4.Enumerate 5 types of graphs?
5.Contrast by illustrative figures: Hamiltonian Cycle and Euler circuit
6.Enumerate various kinds of special graphs
7.Draw a tree and represent its BFS, DFS spanning trees.
8.Write any one shortest path algorithm
Unit 2 questions
1.Enumerate measures for Centrality in social media networks
2.Mention the computational formulae for the following measures of transitivity and reciprocity for analyzing social networks linking behavior.
(a)global clustering coefficient (b)local clustering coefficient (c)reciprocity
3.Elucidate (a)structural equivalence (b)regular equivalence for the similarity between two nodes when using social network information.
4.Elucidate properties of real-world networks
5.Elucidate about random graphs
6.Elucidate about small world model
7.Elucidate about Preferential Attachment Model
Unit 3 question(from 50% syllabus of unit 3)
8.Contrast: Supervised learning Vs. Unsupervised learning
MID exam 1 paper(descriptive) Social Media Mining subject
1.Contrast by illustrative figures: Hamiltonian Cycle and Euler circuit
2.Draw a tree and represent its BFS, DFS spanning trees.
3.Write any one shortest path algorithm
4.Elucidate about Small World Model
5.Elucidate about Preferential Attachment Model
6.Contrast: Supervised learning Vs. Unsupervised learning
MID exam 1 paper(QUIZ/Objective) Social Media Mining subject
__________ is a novel challenge encountered in social media mining.
(a)big data paradox (b)Obtaining Sufficient Samples (c)Noise removal efficiency (d)Evaluation dilemma (e)all of these
2. The process of representing, analysing, and extracting actionable patterns from social media data is
(a)KDD (b)Web mining (c)Social Media Mining
3.The summation of degrees in an undirected graph is twice the number of edges. This statement is
(a)False (b)Fuzzy (c)True
6.In an undirected graph, there are an ______________ (odd/even) number of nodes having odd degre
7.In any directed graph, the summation of in-degrees is ____________ the summation of out-degrees
(a)grater than (b)less than (c)equal to
8._____________________ is a way for representing graphs
(a)adjacency matrix (b)adjacency list (c)none of these
9. _____________ is a shortest path algorithm
(a)BFS (b)DFS (c)Dijkstra's algorithm
10.________ are edges whose removal makes formerly connected components disconnected
(a)nodes (b)branches (c)bridges or cut edges
11._________________(Degree/Katz/Eigenvector centrality) tries to generalize degree centrality by incorporating the importance of the neighbors (or incoming neighbors in directed graphs).
12._____________ (Reciprocity/Transitivity) is when a friend of my friend is my friend
13._________________________ (random graph model/small-world model/preferential attachment model/all of these)
is/are a principal network model(s).
15._____________ (degree distribution/clustering coefficient/average path length/all of these) is/are measurement(s) across real-world networks
16._____________ (degree distribution/clustering coefficient/average path length/all of these) is/are property(s) of the small-world model/preferential attachment model
17.Supervised learning can be divided into classification and regression. When the class attribute is discrete, it is called classification( (regression/classification); when the class attribute is continuous, it is _________ (classification/regression).
18.When labels are discrete, the supervised learning is called (regression/classification) and when labels are real numbers, it is called regression
19.For any connected graph, the spanning tree is a subgraph and a tree that includes all the nodes of the graph False/True
20.__________________ (KDD/Data mining) provides the necessary tools for discovering patterns in data.
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