Social Media Mining-mid1exam paper FINAL-Jun2024

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

1. __________ 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.