Module1:Social Networks-PG(Computer Network Engineering)

Social Networks Module1

1. Course title: Social Networks Course code: MCN2E306 Credits : 3 1

2. SOCIAL NETWORKS (3:0:0) (Elective) Sub code : MCN2E306 CIE: 50Marks Hrs/week : 03 SEE: 50%Marks SEE Hrs: 03 Max. Marks: 100   Course Outcomes: On successful completion of the course, the students will be able to: 1 . Interpret the network structure by applying the concepts of graph theory. 2. Formulate the behavioral models in various environments of social networks. (Module2&3) 3. Illustrate link analysis and cascading behavior in social networks. 4. Describe interactions in Social networks. 2

3. Reference text books Networks, Crowds and Markets by David Easley and Jon Kleinberg, Cambridge University Press, 2010 Social and Economic Networks by Matthew O.Jackson, Princeton University Press,2010. 3

4. Module1 : Content Overview: Aspects of Networks Central Themes and Topics, Central Themes and Topics. Graph Theory and Social Networks Graphs Basic Definitions, Paths and Connectivity, Datasets: An Overview Self Learning Exercise: Distance and Breadth-First Search Network 4

5. Overview of Social Networks Network : A pattern of interconnections among a set of things Social network : The collections of social ties among friends (distant travel, global communication, and digital interaction) Information : publishers, news organizations, the academy. 5

6. Aspects of Networks A network is any collection of objects in which some pairs of these objects are connected by links 6

7. Behaviour and Dynamics Consider structure of network. The “ connectedness ” of a complex system, have two related issues. connectedness at the level of structure — who is linked to whom connectedness at the level of behaviour — the fact that each individual’s actions have implicit consequences for the outcomes of everyone in the system. we also need a framework for reasoning about behaviour and interaction in network contexts. As a result, models of networked behavior must take strategic behavior and strategic reasoning into account 7

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9. A Confluence of Ideas Understanding highly connected systems requires a set of ideas for reasoning about network structure, strategic behaviour, and the feedback effects they produce across large populations. In a network setting, you should evaluate your actions not in isolation, but with the expectation that the world will react to what you do- cause- and-effect relationship. 9

10. Central Themes and Topics Graph theory - is the study of network structure Game theory- provides models of individual behaviour in settings where outcomes depend on the behaviour of others. 10

11. Graph theory Strong ties - representing close and frequent social contacts, tend to be embedded in tightly-linked regions of the network Weak ties - representing more casual and distinct social contacts, tend to cross between different regions Source of conflict : 11

12. Game theory Game theory starts from the observation that, a group of people must simultaneously choose how to act, knowing that the outcome will depend on the joint decisions made by all of them. Ex 1: the problem of choosing a driving route through a network of highways at a time when traffic is heavy. If you’re a driver in such a situation, the delays you experience depend on the pattern of traffic congestion arising not just from your choice of route, but from the choices made by all other drivers as well. 12

13. Game theory continued.. EX 2: The problem of bidding in an auction. If a seller is trying to sell a single item using an auction, then the success of any one bidder in the auction depends not just on how she bids but on how everyone else bids as well — and so an optimal bidding strategy should take this into account. 13

14. Outcomes Markets and strategic interaction in network Information Networks ( search engines: web pages: authors) Institutions and aggregate behaviours. ( rules, conventions, mechanisms) 14

15. Network dynamics ( growth) Population effect 2) Structural effect Population Effects : If we observe a large population over time, we’ll see a recurring pattern by which new ideas, beliefs, opinions, innovations, technologies, products, and social conventions are constantly emerging and evolving. The way in which new practices spread through a population depends in large part on the fact that people influence each other’s behavior. In short, as you see more and more people doing something, you generally become more likely to do it as well. Imitate others decision . 15

16. Structural Effects : the question of how people influence each other’s behaviour is already quite subtle even when the actual structure of the underlying network is left implicit. But taking network structure into account provides important further insights into how such kinds of influence take place. cascading effects- where a new behaviour starts with a small set of initial adopters, and then spreads radially outward through the network. 16

17. Graph theory and social networks Basic definition Graph - A graph consists of a set of objects, called nodes, with certain pairs of these objects connected by links called edges. Representation of some related objects 17

18. Graph as models of networks Graph as models of networks 18

19. Paths and Connectivity Paths: a path is simply a sequence of nodes with the property that each consecutive pair in the sequence is connected by an edge Cycles: a cycle is a path with at least three edges, in which the first and last nodes are the same, but otherwise all nodes are distinct. Connected graph: a graph is connected if for every pair of nodes, there is a path between them 19

20. Components ( Giant components): connected component of a graph (often shortened just to the term “component”) is a subset of the nodes such that: (i) every node in the subset has a path to every other; and (ii) the subset is not part of some larger set with the property that every node can reach every other 20

21. The small world phenomenon The idea that the world looks “small” when you think of how short a path of friends it takes to get from you to almost anyone else Six degrees of separation  is the idea that all people on average are  six , or fewer,  social  connections away from each other 21

22. Network Datasets: An Overview Why you might study Network dataset?? ( research perspective) Reasons you may care about the actual domain it comes from , so that fine-grained details of the data itself are potentially as interesting as the broad picture. you’re using the dataset as a proxy for a related network that may be impossible to measure you’re trying to look for network properties that appear to be common across many different domains, and so finding a similar effect in unrelated settings 22

23. Collaboration graph Collaboration graphs record who works with whom in a specific setting. Ex 1: co-authorships among scientists and co-appearance in movies by actors and actresses are two examples of collaboration graphs Ex 2: Highly placed people in corporate world 23

24. Who-talks-to-Whom Graphs. Large community engaged in several billion conversations over the course of a month. In this way, it captures the “who-talks-to-whom” structure of the community. Email logs: Similar datasets have been constructed from the e-mail logs within a company or a university , as well as from records of phone calls. Economic Network Measurements: Related to this kind of “who-talks-to-whom” data, economic network measurements recording the “who-transacts-with-whom” structure of a market or financial community has been used to study the ways in which different levels of access to market participants can lead to different levels of market power and different prices for goods. 24

25. Information Linkage Graphs Web graphs: Snapshots of the Web are central examples of network datasets; nodes are Web pages and directed edges represent links from one page to another. 25

26. Technological network graph Although the Web is built on a lot of sophisticated technology, it would be a mistake to think of it primarily as a technological network. Examples include the interconnections among computers on the Internet or among generating stations in a power grid. Typical computer network 26

27. Networks in the Natural World. Food webs represent the who-eats-whom relationships among species in an ecosystem: there is a node for each species, and a directed edge from node A to node B indicates that members of A consume members of B. Another heavily-studied network in biology is the structure of neural connections within an organism’s brain: the nodes are neurons, and an edge represents a connection between two neurons. Many more…….. SLE: Distance and Breadth-first search 27