Networks in their surrounding contexts


Published on

Simplified slides of content in chapter 4 titled "Networks in their surrounding contexts" related to information networks.

Published in: Education
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Networks in their surrounding contexts

  1. 1. Networks in Their Surrounding Contexts
  2. 2. Overview • Previously we learnt about – Different structures characterizing the social network – Typical process that affect formation of links. • Now, – Various contexts that show impact on social n/w. – Surrounding contexts: outside nodes and edges of n/w
  3. 3. Homophily • Homophily : the principle that we tend to be similar to our friends • your friends are generally similar to you along – Racial and ethnic dimensions. – Age – Mutable characteristics like • Places they live • Their occupations • Levels of influence • Interests • Beliefs • opinions
  4. 4. Example • Consider 2 cases: – A friendship that forms because two people are introduced through a common friend – A friendship that forms because two people attend the same school or work for the same company. – 1. Intrinsic to network. – 2.Beyond the network.
  5. 5. produced by James Moody
  6. 6. Triadic Closure and Homophily • B and C has a common friend A. • since we know that A-B and A-C friendships already exist • The principle of homophily suggests – B and C are likely to be similar. – there is an elevated chance that a B-C friendship will form – even if neither of them is aware that the other one knows A.
  7. 7. Measuring Homophily
  8. 8. • Homophily Test: – If the fraction of cross-gender edges is significantly less than 2pq, then there is evidence for homophily • In previous example 5 of the 18 edges in the graph are cross-gender. Since p = 2/3 and q = 1/3 • we should be comparing the fraction of cross-gender edges to 2pq = 4/9 = 8/18.(for no Homophily) But actually only 5, so homophily
  9. 9. Inverse Homophily • If the probability of Cross Similarities(in this example cross gender) greater than 2pq, then it is called as INVERSE HOMOPHILY
  10. 10. Mechanisms Underlying Homophily: Selection and Social Influence • Immutable Characteristics. – Race or Ethnicity. • Mutable Characteristics. – behaviors, activities, interests, beliefs, and opinions. – feedback effects between people's individual characteristics 1. Selection 2. Socialization and Social influence
  11. 11. Selection and Social influence • With selection, the individual characteristics drive the formation of links. • With social influence, the existing links in the network serve to shape people's (mutable) characteristics.
  12. 12. The Interplay of Selection and Social Influence • In a single snapshot of a network, It is very hard to sort out the distinct effects and relative contributions of selection and social influence. • Questions that arise: – Have the people in the network adapted their behaviors to become more like their friends? – have they sought out people who were already like them? Answers can be found through Longitudinal study of social network
  13. 13. Longitudinal study • The social connections and the behaviors within a group are both tracked over a period of time. • This makes it possible to check his – Behavioral changes that occur after changes in network connections. – Changes in network that occur after changes in individual behavior.
  14. 14. Examples • Pairs of adolescent friends to have similar outcomes – In terms of scholastic achievement – In delinquent behavior such as drug use • Normally in teenagers, both selection and social influence have a natural resonance. • Its hard to find how these two interact and how one is more strongly at work than the other.
  15. 15. Affiliation • Till now, – We have been seeing that these contexts effecting the network from outside. – It is possible to put these contexts into the network itself, by working with a larger network that contains both people and contexts as nodes.
  16. 16. Focal Points • Being part of a – particular company – Neighborhood – Frequenting a particular place – Pursuing a particular hobby or interest • Such activities can be considered as foci--The Focal points of social interaction--constituting social, physiological, legal, physical entities around which joint activities are organized.
  17. 17. Affiliation Networks Bipartite Graph: We say that a graph is bipartite if its nodes can be divided into two sets in such a way that every edge connects a node in one set to a node in the other set
  18. 18. Use of Affiliation Networks
  19. 19. Co-Evolution of Social and Affiliation Networks • It's clear that both social networks and affiliation networks change over time: • new friendship links are formed, and • People become associated with new foci. • If two people participate in a shared focus, • This provides them with an opportunity to become friends; • If two people are friends, they can influence each other's choice of foci
  20. 20. Social-Affiliation Network • As of now we have nodes for people and nodes for foci, but we now introduce two distinct kinds of edges as well. • The first kind of edge functions as an edge in a social network: it connects two people, and indicates friendship • The second kind of edge functions as an edge in an affiliation network: it connects a person to a focus, and indicates the participation of the person in the focus
  21. 21. Closure Process
  22. 22. Example (i) Bob introduces Anna to Claire. (ii) Karate introduces Anna to Daniel. (iii) Anna introduces Bob to Karate.
  23. 23. Tracking Link Formation in On-Line Data • The deeper quantitative understanding of how mechanisms of link formation operate in real life. • Exploring the previous questions in a broader range of large datasets is an important problem. • Consider triadic Closure, lets have 2 questions – How much more likely is a link to form between two people in a social network if they already have a friend in common? – How much more likely is an edge to form between two people if they have multiple friends in common?
  24. 24. Example Anna and Esther have two friends in common, while Claire and Daniel only have one friend in common. How much more likely is the formation of a link in the rest of these two cases?
  25. 25. Way of finding relation
  26. 26. Quantifying the effects of triadic closure in an e-mail dataset
  27. 27. • Forming a link each day due to 1 common friend is p(independently) • So failing to form a link is 1-p, • For K friends in common, the failing probability for k trials is (1-p)k • So probability that link forms is • This is the dotted line In previous graph.
  28. 28. Quantifying the effects of focal closure in an e-mail dataset
  29. 29. Quantifying the effects of membership closure
  30. 30. Quantifying the Interplay Between Selection and Social Influence • In Wikipedia, if we consider two editors A and B, the relation can be quantified as • For example, if editor A has edited the Wikipedia articles on Ithaca NY and Cornell University, and editor B has edited the articles on Cornell University and Stanford University, then their similarity under this measure is 1=3, since they have jointly edited one article (Cornell) out of three that they have edited in total (Cornell, Ithaca, and Stanford).
  31. 31. Conclusion • Overall, then, these analyses represent early attempts to quantify some of the basic mechanisms of link formation at a very large scale, using on-line data • But, In particular, it natural to ask whether the general shapes of the curves in previous graphs are similar across different domains – including domains that are less technologically mediated – whether these curve shapes can be explained at a simpler level by more basic underlying social mechanisms.