B.E Computer Science and Engineering CS 6010 -Social Networks Analysis QUESTION BANK UNIT – 1 PART A 1. Define semantic Web. The Semantic Web is an extension of the Web through standards by the World Wide Web Consortium (W3C). The standards promote common data formats and exchange protocols on the Web, most fundamentally the Resource Description Framework (RDF). 2. Give the limitations of current Web. There is a general consent that the Web is one of the greatest inventions of the 20 th Century. But could it be better? The reason that we do not often raise this question any more has to do with our unusual ability to adapt to the limitations of our information systems. In the case of the Web this means adaptation to our primary interface to the vast information that constitutes the Web: the search engine. In the following we list four questions that search engines cannot answer at the moment with satisfaction or not at all. 3. What is social network analysis? Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. 4. List the Static Properties of social networks. Static Unweighted Graphs Heavy-tailed Degree Distribution Small Diameter Eigenvalue Power Law (EPL) Triangle Power Law (TPL) Community Structure Static Weighted Graphs Weight Power Law (WPL) Edge Weights Power Law Snapshot Power Laws (SPL) 5. Define small-world phenomenon. The small-world phenomenon -- the principle that we are all linked by short chains of acquaintances, or "six degrees of separation" -- is a fundamental issue in social networks; it is a basic statement about the abundance of short paths in a graph whose nodes are people, with links joining pairs who know one another 6. State Eigen Value Power Law. The eigenvalues of a graph are defined as the eigenvalues of its adjacency matrix. The set of eigenvalues of a graph is called a graph spectrum. CS6010 – SOCIAL NETWORK ANALYSIS AP/IT- MAMCE,TRI-621 105
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(For a matrix A, if there is a vector X s.t. AX = X for some scalar, then is the eigenvalue of A assoc with eigenvector X.) EPL states that the 20 or so largest eigenvalues of the Internet graph are power-law distributed. It has been shown that the Eigenvalue Power Law is a consequence of the Degree Power Law.
7. How the dynamic properties of social networks are studied? These are typically studied by looking at a series of static snapshots and seeing how measurements of these snapshots compare 8. What is a Core-Periphery (C/P) structure? Core-periphery structures are commonly found in economic and social networks. They consist of a dense cohesive core and a sparse, loosely connected periphery. (Zhang, Martin, & Newman, n.d.) Networks can be described from various macro, micro and meso scales. Identifying these structures allows for the comparison between complex structures. 9. Give the components of ontologies. Concepts, also called Classes, Types or Universals are a core component of most ontologies. A Concept represents a group of different Individuals, that share common characteristics, which may be more or less specific. 10. What do you mean by Heavy-tailed degree distribution? In probability theory, heavy-tailed distributions are probability distributions whose tails are not exponentially bounded. that is, they have heavier tails than the exponential distribution. In many applications it is the right tail of the distribution that is of interest, but a distribution may have a heavy left tail, or both tails may be heavy. There are three important subclasses of heavy-tailed distributions: the fat-tailed distributions, the long-tailed distributions and the subexponential distributions. In practice, all commonly used heavy-tailed distributions belong to the subexponential class. 11. State Triangle Power Law. The number of triangles ∆ follows a power-law in the form of f(∆) ∝ ∆σ, with the exponent σ < 0. The number of nodes that participate in ∆ number of triangles follows a power-law in the form of f(∆) ∝ ∆σ , with the exponent σ < 0. TPL means that Many nodes have only a few triangles in their neighborhoods and A few nodes participate in many numbers of triangles with their neighbors. 12. Write notes on static weighted graphs. The patterns in dynamic time-evolving graphs that do not consider edge weights include the shrinking diameter property, the densifcation law, oscillating around a constant size CS6010 – SOCIAL NETWORK ANALYSIS AP/IT- MAMCE,TRI-621 105
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secondary largest connected components, the largest eigenvalue law and the bursty and selfsimilar edge additions over time. 13. State Weight Power Law. We consider weighted directed graphs Data set: records in the form (IP-source, IP destination, timestamp, number of-packets) We can have multi-edges and weights Notations:
W(t): the total weight up to time t E(t): the number of distinct edges up to time t Ed(t): the number of multi-edges (d stands for duplicate edges) up to time t N(t): the number of nodes up to time t
14. State Edge Weights Power Law. Given a real-world graph, nodes i and j with weights wi and wj , the edge ei,j with weight wi,j , then we have the power law This means that the weight of a given edge and weights of its neighboring two nodes are correlated (similar to Newton’s Gravitational Law). 15. Explain Snapshot Power laws. Consider the i-th node of a weighted graph, at time t (a snapshot), and let out i , outwi be its out-degree and out-weight. Then o Where ow is the out-weight-exponent of the SPL. Similarly, for the in-degree, with inweight-exponent iw. The exponents iw and ow take values in the range [0.9-1.2] and [0.95-1.35], respectively. The exponent over time remains almost constant. 16. Write notes on shrinking diameter. It can be observed that not only is the diameter of real graphs small, but it also shrinks and then stabilizes over time.There is a ‘gelling point’ at which many small disconnected components merge and form the largest connected component in the graph. 17. State Densification Power Law. The Community Guided Attachment leads to Densification Power Law with exponent
18. Define the term: Degree Centrality. CS6010 – SOCIAL NETWORK ANALYSIS AP/IT- MAMCE,TRI-621 105
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Historically first and conceptually simplest is degree centrality, which is defined as the number of links incident upon a node (i.e., the number of ties that a node has). The degree can be interpreted in terms of the immediate risk of a node for catching whatever is flowing through the network (such as a virus, or some information). In the case of a directed network (where ties have direction), we usually define two separate measures of degree centrality, namely indegree and outdegree. Accordingly, indegree is a count of the number of ties directed to the node and outdegree is the number of ties that the node directs to others. When ties are associated to some positive aspects such as friendship or collaboration, indegree is often interpreted as a form of popularity, and outdegree as gregariousness. The degree centrality of a vertex , for a given graph G: (V,E) with V vertices and E edges, is defined as
19. Define the terms: Closeness Centrality, local Closeness Centrality. An actor is considered important if he/she is relatively close to all other actors. Sum of geodesic distances to all other nodes. Inverse measure of centrality It is a measure of reach, i.e. the speed with which information can reach other nodes from a given starting node 20. Define the terms: betweenness Centrality. Betweenness is a centrality measure of a vertex within a graph (there is also edge betweenness, which is not discussed here). Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. It was introduced as a measure for quantifying the control of a human on the communication between other humans in a social network by Linton Freeman In his conception, vertices that have a high probability to occur on a randomly chosen shortest path between two randomly chosen vertices have a high betweenness. 21. What is a clique? 22. Define the terms: Degree and density. The density of a graph G = (V,E) measures how many edges are in set E compared to the maximum possible number of edges between vertices in set V. Density is calculated as follows:
An undirected graph has no loops and can have at most |V| * (|V| − 1) / 2 edges, so the density of an undirected graph is 2 * |E| / (|V| * (|V| − 1)). A directed graph has no loops and can have at most |V| * (|V| − 1) edges, so the density of a directed graph is |E| / (|V| * (|V| − 1)) The average degree of a graph G is another measure of how many edges are in set E compared to number of vertices in set V. Because each edge is incident to two vertices and counts in the degree of both vertices, the average degree of an undirected graph is 2*|E|/|V|. 23. Define centrality In graph theory and network analysis, indicators of centrality identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, and superspreaders of disease. CS6010 – SOCIAL NETWORK ANALYSIS AP/IT- MAMCE,TRI-621 105
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24. Define clustering coefficient. In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterised by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established between two nodes 25. Give the components of social networks. PART B 1. What are the limitations of current Web? Explain the development of semantic Web and the emergence of Social Web. 2. Briefly explain the development of Social Network Analysis. 3. Enumerate the static properties of social networks. 4. Explain the dynamic properties of social networks. 5. Illustrate the Global structure of networks with an example. 6. Discuss in detail about the macro-structure of social networks. 7. Enumerate the different dimensions of social capital and their related concepts and measures. 8. Briefly explain the following: a) Electronic discussion networks b) Blogs and online communities c) Web-based Networks d) Personal Networks 9. Explain the statistical properties of social network analysis. 10. Discuss the business applications of Social Network Analysis.
CS6010 – SOCIAL NETWORK ANALYSIS AP/IT- MAMCE,TRI-621 105
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