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  • Source: Albert and Barabasi, “Statistical mechanics of complex networks.” Review of Modern Physics . 74:48-94. (2002)
  • When I first decided to move to India, I wondered about how I could make new friends.
  • First, I met Bash at Microsoft, through an NGO called Asha for Eduation.
  • Bash suggested I contact Asha’s Bangalore chapter. On my second day in India, I ended up going on a field trip with Asha Bangalore, and met Ranjeet. We started to talk, and when I mentioned I had gone to graduate school at Yale, he said, “I know one person from Yale, but I think he was there earlier than you. His name was Sharad.”
  • Sharad was my roommate at Yale.
  • (Actually, Amitabh is closer to Kevin, but the minimal path of 4 uses two actors who always play minor parts in movies, so I used this path, which involves better known actors.)
  • Source: Watts (2003), Six Degrees ; Barabasi (2003), Linked .
  • Ben and I are good friends from college. Ben wrote a book, Bringing Down the House , for which the movie rights were sold to Kevin Spacey (I’ve seen a photo of Ben and Kevin posing in a picture with Hugh Hefner, of all people). Kevin is two movies away from Kevin Bacon.
  • Source: http://www.oakland.edu/enp/trivia.html
  • Milgram was a brilliant psychologist, notorious for his studies of obedience, where subjects continued to deliver what they believed to be painful electric shocks, just because a person in a lab coat told them they should do so as part of an experiment. Note on the small-worlds experiment: – Not many of the letters in the original experiment arrived. -- The experiment presaged some later discoveries, including hubs, tendency to go by geography and profession, etc.
  • This link to Milgram is my best guess, based on information I could find on the Internet: Allan Wagner was at the Yale Psychology department through 1961-1962, when Milgram did his experiments. Sternberg was chairman of the psych dept in 1992, during which time, both Allan Wagner and Mike Tarr were in the same department. I know Mike.
  • This is similar to phase transitions in physics, when gases become liquids and liquids become solids. (Even Bose-Einstein condensation is also relevant, it seems.)
  • The ln(N) expression is consistent with intuition about branching factors for trees. Even with lots of node repetition, if only 10% of each person’s contacts are not duplicates, links would rapidly span the entire population. I know Peter from the computer vision community. Peter was a student of Mumford’s, I think. David Mumford is a Fields-Medal-winning mathematician, who has singlehandedly reduced the Erdos number of most computer vision researchers by doing work in computer vision. Fan Chung is a mathematician who has authored papers with both Erdos and Mumford.
  • Source: Albert and Barabasi, “Statistical mechanics of complex networks.” Review of Modern Physics . 74:48-94. (2002)
  • Source: Albert and Barabasi, “Statistical mechanics of complex networks.” Review of Modern Physics . 74:48-94. (2002)
  • Source: Jon Kleinberg, “Navigation in a small world.” Science, 406:845 (2000).
  • Source: Jon Kleinberg, “Navigation in a small world.” Science, 406:845 (2000).
  • Source: Jon Kleinberg, “Navigation in a small world.” Science, 406:845 (2000).
  • Source: Duncan Watts, Six Degrees (2003). I know Ramin as a computer vision researcher. Ramin is in the CS department at Cornell; I assume he knows Jon Kleinberg.
  • Milgram was a brilliant psychologist, notorious for his studies of obedience, where subjects continued to deliver what they believed to be painful electric shocks, just because a person in a lab coat told them they should do so as part of an experiment. Note on the small-worlds experiment: – Not many of the letters in the original experiment arrived. -- The experiment presaged some later discoveries, including hubs, tendency to go by geography and profession, etc.
  • Source: Duncan Watts, Six Degrees (2003).
  • Source: Duncan Watts, Six Degrees (2003). Small-world phenomena of random graphs emerges even with less random links. Jonathan Donner will be working with us at MSR India. Jonathan is now at the Earth Institute at Columbia, where he knows Nobuyuki Hanaki, who has co-authored papers with Duncan Watts.

Microsoft Research, India Social Networks And Their Applications To Web (Tin180 Com) Presentation Transcript

  • 1. Social Networks First half based on slides by Kentaro Toyama, Microsoft Research, India And their applications to Web
  • 2. Networks—Physical & Cyber Typhoid Mary (Mary Mallon) Patient Zero (Gaetan Dugas)
  • 3. Applications of Network Theory
    • World Wide Web and hyperlink structure
    • The Internet and router connectivity
    • Collaborations among…
      • Movie actors
      • Scientists and mathematicians
    • Sexual interaction
    • Cellular networks in biology
    • Food webs in ecology
    • Phone call patterns
    • Word co-occurrence in text
    • Neural network connectivity of flatworms
    • Conformational states in protein folding
  • 4. Web Applications of Social Networks
    • Analyzing influence/importance
      • Page Rank
        • Related to recursive in-degree computation
      • Authorities/Hubs
    • Discovering Communities
      • Finding near-cliques
    • Analyzing Trust
      • Propagating Trust
      • Using propagated trust to fight spam
        • In Email
        • In Web page ranking
    • Web pages (and link-structure)
    • Online social networks (FOAF networks such as ORKUT, myspace etc)
    • Blogs
  • 5.  
  • 6. Society as a Graph People are represented as nodes.
  • 7. People are represented as nodes. Relationships are represented as edges. (Relationships may be acquaintanceship, friendship, co-authorship, etc.) Society as a Graph
  • 8. People are represented as nodes. Relationships are represented as edges. (Relationships may be acquaintanceship, friendship, co-authorship, etc.) Allows analysis using tools of mathematical graph theory Society as a Graph
  • 9. History (based on Freeman, 2000)
    • 17th century: Spinoza developed first model
    • 1937: J.L. Moreno introduced sociometry; he also invented the sociogram
    • 1948: A. Bavelas founded the group networks laboratory at MIT; he also specified centrality
  • 10. History (based on Freeman, 2000)
    • 1949: A. Rapaport developed a probability based model of information flow
    • 50s and 60s: Distinct research by individual researchers
    • 70s: Field of social network analysis emerged.
      • New features in graph theory – more general structural models
      • Better computer power – analysis of complex relational data sets
  • 11. Graphs – Sociograms (based on Hanneman, 2001)
    • Strength of ties:
      • Nominal
      • Signed
      • Ordinal
      • Valued
  • 12. Visualization Software: Krackplot Sources: http://www.andrew.cmu.edu/user/krack/krackplot/mitch-circle.html http://www.andrew.cmu.edu/user/krack/krackplot/mitch-anneal.html
  • 13. Connections
    • Size  
      • Number of nodes
    • Density
      • Number of ties that are present the amount of ties that could be present
    • Out-degree
      • Sum of connections from an actor to others
    • In-degree
      • Sum of connections to an actor
  • 14. Distance
    • Walk
      • A sequence of actors and relations that begins and ends with actors
    • Geodesic distance
      • The number of relations in the shortest possible walk from one actor to another
    • Maximum flow
      • The amount of different actors in the neighborhood of a source that lead to pathways to a target
  • 15. Some Measures of Power & Prestige (based on Hanneman, 2001)
    • Degree
      • Sum of connections from or to an actor
        • Transitive weighted degree  Authority, hub, pagerank
    • Closeness centrality
      • Distance of one actor to all others in the network
    • Betweenness centrality
      • Number that represents how frequently an actor is between other actors’ geodesic paths
  • 16. Cliques and Social Roles (based on Hanneman, 2001)
    • Cliques
      • Sub-set of actors
        • More closely tied to each other than to actors who are not part of the sub-set
          • (A lot of work on “trawling” for communities in the web-graph)
          • Often, you first find the clique (or a densely connected subgraph) and then try to interpret what the clique is about
    • Social roles
      • Defined by regularities in the patterns of relations among actors
  • 17. Outline
    • Small Worlds
    • Random Graphs
    • Alpha and Beta
    • Power Laws
    • Searchable Networks
    • Six Degrees of Separation
  • 18. Outline
    • Small Worlds
    • Random Graphs
    • Alpha and Beta
    • Power Laws
    • Searchable Networks
    • Six Degrees of Separation
  • 19. Trying to make friends Kentaro
  • 20. Trying to make friends Kentaro Bash Microsoft
  • 21. Trying to make friends Kentaro Ranjeet Bash Microsoft Asha
  • 22. Trying to make friends Kentaro Ranjeet Bash Sharad Microsoft Asha New York City Yale Ranjeet and I already had a friend in common!
  • 23. I didn’t have to worry… Kentaro Bash Karishma Sharad Maithreyi Anandan Venkie Soumya
  • 24. It’s a small world after all! Kentaro Ranjeet Bash Karishma Sharad Maithreyi Anandan Prof. Sastry Venkie PM Manmohan Singh Prof. Balki Pres. Kalam Prof. Jhunjhunwala Dr. Montek Singh Ahluwalia Ravi Dr. Isher Judge Ahluwalia Pawan Aishwarya Prof. McDermott Ravi’s Father Amitabh Bachchan Prof. Kannan Prof. Prahalad Soumya Nandana Sen Prof. Amartya Sen Prof. Veni Rao
  • 25. The Kevin Bacon Game
    • Invented by Albright College students in 1994:
      • Craig Fass, Brian Turtle, Mike Ginelly
    • Goal: Connect any actor to Kevin Bacon, by linking actors who have acted in the same movie.
    • Oracle of Bacon website uses Internet Movie Database (IMDB.com) to find shortest link between any two actors:
    • http:// oracleofbacon.org /
    Boxed version of the Kevin Bacon Game
  • 26. The Kevin Bacon Game
    • Kevin Bacon
    An Example Tim Robbins Om Puri Amitabh Bachchan Yuva (2004) Mystic River (2003) Code 46 (2003) Rani Mukherjee Black (2005)
  • 27. … actually Bachchan has a Bacon number 3
    • Perhaps the other path is deemed more diverse/ colorful…
  • 28. The Kevin Bacon Game
    • Total # of actors in database: ~550,000
    • Average path length to Kevin: 2.79
    • Actor closest to “center”: Rod Steiger (2.53)
    • Rank of Kevin, in closeness to center: 876 th
    • Most actors are within three links of each other!
    Center of Hollywood?
  • 29. Not Quite the Kevin Bacon Game
    • Kevin Bacon
    Aidan Quinn Kevin Spacey Kentaro Toyama Bringing Down the House (2004) Cavedweller (2004) Looking for Richard (1996) Ben Mezrich Roommates in college (1991)
  • 30. Erdős Number (Bacon game for Brainiacs  )
    • Number of links required to connect scholars to Erdős, via co-authorship of papers
    • Erdős wrote 1500+ papers with 507 co-authors.
    • Jerry Grossman’s (Oakland Univ.) website allows mathematicians to compute their Erdos numbers:
    • http:// www.oakland.edu/enp /
    • Connecting path lengths, among mathematicians only:
      • average is 4.65
      • maximum is 13
    Paul Erdős (1913-1996) Unlike Bacon, Erdos has better centrality in his network
  • 31. Erdős Number
    • Paul Erdős
    Dimitris Achlioptas Bernard Schoelkopf Kentaro Toyama Andrew Blake Mike Molloy Toyama, K. and A. Blake (2002). Probabilistic tracking with exemplars in a metric space. International Journal of Computer Vision . 48(1):9-19. Romdhani, S., P. Torr, B. Schoelkopf, and A. Blake (2001). Computationally efficient face detection. In Proc. Int’l. Conf. Computer Vision , pp. 695-700. Achlioptas, D., F. McSherry and B. Schoelkopf. Sampling Techniques for Kernel Methods. NIPS 2001 , pages 335-342. Achlioptas, D. and M. Molloy (1999). Almost All Graphs with 2.522 n Edges are not 3-Colourable. Electronic J. Comb. (6), R29. Alon, N., P. Erdos, D. Gunderson and M. Molloy (2002). On a Ramsey-type Problem. J. Graph Th. 40, 120-129. An Example
  • 32. ..and Rao has even shorter distance 
  • 33. ..collaboration distances
  • 34. Six Degrees of Separation
    • The experiment:
    • Random people from Nebraska were to send a letter (via intermediaries) to a stock broker in Boston.
    • Could only send to someone with whom they were on a first-name basis.
    • Among the letters that found the target, the average number of links was six.
    Milgram (1967) Stanley Milgram (1933-1984) Many “issues” with the experiment…
  • 35. Some issues with Milgram’s setup
    • A large fraction of his test subjects were stockbrokers
        • So are likely to know how to reach the “goal” stockbroker
    • A large fraction of his test subjects were in boston
        • As was the “goal” stockbroker
    • A large fraction of letters never reached
        • Only 20% reached
  • 36. Six Degrees of Separation
    • John Guare wrote a play called Six Degrees of Separation, based on this concept.
    Milgram (1967) “ Everybody on this planet is separated by only six other people. Six degrees of separation. Between us and everybody else on this planet. The president of the United States. A gondolier in Venice… It’s not just the big names. It’s anyone. A native in a rain forest. A Tierra del Fuegan. An Eskimo. I am bound to everyone on this planet by a trail of six people…” Robert Sternberg Mike Tarr Kentaro Toyama Allan Wagner ?
  • 37. Outline
    • Small Worlds
    • Random Graphs--- Or why does the “small world” phenomena exist?
    • Alpha and Beta
    • Power Laws
    • Searchable Networks
    • Six Degrees of Separation
  • 38. 9/30
  • 39. Random Graphs
    • N nodes
    • A pair of nodes has probability p of being connected.
    • Average degree, k ≈ pN
    • What interesting things can be said for different values of p or k ?
    • (that are true as N  ∞ )
    Erdős and Renyi (1959) p = 0.0 ; k = 0 N = 12 p = 0.09 ; k = 1 p = 1.0 ; k ≈ ½ N 2
  • 40. Random Graphs Erdős and Renyi (1959) p = 0.0 ; k = 0 p = 0.09 ; k = 1 p = 1.0 ; k ≈ N p = 0.045 ; k = 0.5 Let’s look at… Size of the largest connected cluster Diameter (maximum path length between nodes) of the largest cluster If Diameter is O(log(N)) then it is a “Small World” network Average path length between nodes (if a path exists)
  • 41. An examlple random graph p=0.01
  • 42. Random Graphs Erdős and Renyi (1959) p = 0.0 ; k = 0 p = 0.09 ; k = 1 p = 1.0 ; k ≈ N p = 0.045 ; k = 0.5 Size of largest component Diameter of largest component Average path length between (connected) nodes 1 5 11 12 0 4 7 1 0.0 2.0 4.2 1.0
  • 43. Random Graphs
    • If k < 1:
      • small, isolated clusters
      • small diameters
      • short path lengths
    • At k = 1:
      • a giant component appears
      • diameter peaks
      • path lengths are high
    • For k > 1:
      • almost all nodes connected
      • diameter shrinks
      • path lengths shorten
    Erdős and Renyi (1959) Percentage of nodes in largest component Diameter of largest component (not to scale) 1.0 0 k 1.0 phase transition
  • 44. Random Graphs
    • What does this mean?
    • If connections between people can be modeled as a random graph, then…
      • Because the average person easily knows more than one person (k >> 1),
      • We live in a “small world” where within a few links, we are connected to anyone in the world.
      • Erdős and Renyi showed that average
      • path length between connected nodes is
    Erdős and Renyi (1959) David Mumford Peter Belhumeur Kentaro Toyama Fan Chung
  • 45. Random Graphs
    • What does this mean?
    • If connections between people can be modeled as a random graph, then…
      • Because the average person easily knows more than one person (k >> 1),
      • We live in a “small world” where within a few links, we are connected to anyone in the world.
      • Erdős and Renyi computed average
      • path length between connected nodes to be:
    Erdős and Renyi (1959) David Mumford Peter Belhumeur Kentaro Toyama Fan Chung BIG “IF”!!!
  • 46. Outline
    • Small Worlds
    • Random Graphs
    • Alpha and Beta
    • Power Laws ---and scale-free networks
    • Searchable Networks
    • Six Degrees of Separation
  • 47. Degree Distribution in Random Graphs
    • In a Erdos-Renyi (uniform) Random Graph with n nodes, the probability that a node has degree k is given by
    • As n  infinity, this becomes a Poisson distribution (where  is the mean degree and is equal to pn)
    Both mean and variance are  Unlike normal distribution which has two parameters poisson has only one.. (so fewer degrees of freedom)
  • 48. Degree Distribution & Power Laws
    • But, many real-world networks exhibit a power-law distribution.
    •  also called “Heavy tailed” distribution
    Degree distribution of a random graph, N = 10,000 p = 0.0015 k = 15. (Curve is a Poisson curve, for comparison.) Typically 2<r<3. For web graph r ~ 2.1 for in degree distribution 2.7 for out degree distribution Note that poisson decays exponentially while power law decays polynomially Long tail k -r Sharp drop Rare events are not so rare! Both mean and variance are 
  • 49. Random vs. Real Social networks
    • Random network models introduce an edge between any pair of vertices with a probability p
      • The problem here is NOT randomness, but rather the distribution used (which, in this case, is uniform )
    • Real networks are not exactly like these
      • Tend to have a relatively few nodes of high connectivity (the “Hub” nodes)
      • These networks are called “Scale-free” networks
        • Macro properties scale-invariant
  • 50. Properties of Power law distributions
    • Ratio of area under the curve [from b to infinity] to [from a to infinity] =(b/a) 1-r
      • Depends only on the ratio of b to a and not on the absolute values
      • “ scale-free”/ “self-similar”
    • A moment of order m exists only if r>m+1
    a b
  • 51. Power Laws
    • Power-law distributions are straight lines in log-log space.
        • -- slope being r
        • y=k -r  log y = -r log k  ly= -r lk
    • How should random graphs be generated to create a power-law distribution of node degrees?
    • Hint:
    • Pareto’s * Law: Wealth distribution follows a power law.
    Albert and Barabasi (1999) Power laws in real networks: (a) WWW hyperlinks (b) co-starring in movies (c) co-authorship of physicists (d) co-authorship of neuroscientists * Same Velfredo Pareto, who defined Pareto optimality in game theory.
  • 52. Generating Scale-free Networks..
    • “ The rich get richer!”
    • Power-law distribution of node-degree arises if
    • (but not “only if”)
      • As Number of nodes grow edges are added in proportion to the number of edges a node already has.
        • Alternative: Copy model—where the new node copies a random subset of the links of an existing node
          • Sort of close to the WEB reality
    • Examples of Scale-free networks (i.e., those that exhibit power law distribution of in degree)
    • Social networks , including collaboration networks. An example that has been studied extensively is the collaboration of movie actors in films .
    • Protein -interaction networks.
    • Sexual partners in humans, which affects the dispersal of sexually transmitted diseases .
    • Many kinds of computer networks , including the World Wide Web .
  • 53. Scale-free Networks
    • Scale-free networks also exhibit small-world phenomena
      • For a random graph having the same power law distribution as the Web graph, it has been shown that
        • Avg path length = 0.35 + log 10 N
    • However, scale-free networks tend to be more brittle
      • You can drastically reduce the connectivity by deliberately taking out a few nodes
    • This can also be seen as an opportunity..
      • Disease prevention by quarantaining super-spreaders
        • As they actually did to poor Typhoid Mary..
  • 54. Attacks vs. Disruptions on Scale-free vs. Random networks
    • Disruption
      • A random percentage of the nodes are removed
    • How does the diameter change?
      • Increases monotonically and linearly in random graphs
      • Remains almost the same in scale-free networks
        • Since a random sample is unlikely to pick the high-degree nodes
    • Attack
      • A precentage of nodes are removed willfully (e.g. in decreasing order of connectivity)
    • How does the diameter change?
      • For random networks, essentially no difference from disruption
        • All nodes are approximately same
      • For scale-free networks, diameter doubles for every 5% node removal!
        • This is an opportunity when you are fighting to contain spread…
  • 55. Exploiting/Navigating Small-Worlds
    • Case 1: Centralized access to network structure
      • Paths between nodes can be computed by shortest path algorithms
        • E.g. All pairs shortest path
      • ..so, small-world ness is trivial to exploit..
        • This is what ORKUT, Friendster etc are trying to do..
    • Case 2: Local access to network structure
      • Each node only knows its own neighborhood
      • Search without children-generation function 
      • Idea 1: Broadcast method
        • Obviously crazy as it increases traffic everywhere
      • Idea 2: Directed search
        • But which neighbors to select?
        • Are there conditions under which decentralized search can still be easy?
    How does a node in a social network find a path to another node?  6 degrees of separation will lead to n 6 search space (n=num neighbors)  Easy if we have global graph.. But hard otherwise Computing one’s Erdos number used to take days in the past! There are very few “fully decentralized” search applications. You normally have hybrid methods between Case 1 and Case 2
  • 56. Searchability in Small World Networks
    • Searchability is measured in terms of Expected time to go from a random source to a random destination
      • We know that in Smallworld networks, the diameter is exponentially smaller than the size of the network.
      • If the expected time is proportional to some small power of log N, we are doing well
    • Qn: Is this always the case in small world networks?
    • To begin to answer this we need to look generative models that take a notion of absolute (lattice or coordinate-based) neighborhood into account
    • Kleinberg experimented with Lattice networks (where the network is embedded in a lattice—with most connections to the lattice neighbors, but a few shortcuts to distant neighbors)
    • and found that the answer is “Not always”
    Kleinberg (2000)
  • 57. Summary
    • A network is considered to exhibit small world phenomenon, if its diameter is approximately logarithm of its size (in terms of number of nodes)
    • Most uniform random networks exhibit small world phenomena
    • Most real world networks are not uniform random
      • Their in degree distribution exhibits power law behavior
      • However, most power law random networks also exhibit small world phenomena
      • But they are brittle against attack
    • The fact that a network exhibits small world phenomenon doesn’t mean that an agent with strictly local knowledge can efficiently navigate it (i.e, find paths that are O(log(n)) length
      • It is always possible to find the short paths if we have global knowledge
        • This is the case in the FOAF (friend of a friend) networks on the web
  • 58. Web Applications of Social Networks
    • Analyzing page importance
      • Page Rank
        • Related to recursive in-degree computation
      • Authorities/Hubs
    • Discovering Communities
      • Finding near-cliques
    • Analyzing Trust
      • Propagating Trust
      • Using propagated trust to fight spam
        • In Email
        • In Web page ranking
  • 59. Other Power Laws of Interest to CSE494 If this is the power-law curve about in degree distribution, where is Google page on this curve? Homework 2 will be due next week Mid-term is most likely to be on 10/16 Project 2 will be given by the end of next week
  • 60. Zipf’s Law: Power law distriubtion between rank and frequency
    • In a given language corpus, what is the approximate relation between the frequency of a k th most frequent word and (k+1) th most frequent word?
    For s>1 Most popular word is twice as frequent as the second most popular word! Law of categories in Marketing… Digression Word freq in wikipedia f=1/r
  • 61. What is the explanation for Zipf’s law?
    • Zipf’s law is an empirical law in that it is observed rather than “proved”
    • Many explanations have been advanced as to why this holds.
    • Zipf’s own explanation was “principle of least effort”
      • Balance between speaker’s desire for a small vocabulary and hearer’s desire for a large one (so meaning can be easily disambiguated)
    • Alternate explanation— “rich get richer” –popular words get used more often
    • Li (1992) shows that just random typing of letters with space will lead to a “language” with zipfian distribution..
  • 62. Heap’s law: A corollary of Zipf’s law
    • What is the relation between the size of a corpus (in terms of words) and the size of the lexicon (vocabulary)?
      • V = K n b
      • K ~ 10—100
      • b ~ 0.4 – 0.6
        • So vocabulary grows as a square root of the corpus size..
    Explanation? --Assume that the corpus is generated by randomly picking words from a zipfian distribution.. Notice the impact of Zipf on generating random text corpuses!
  • 63. Benford’s law (aka first digit phenomenon)
    • How often does the digit 1 appear in numerical data describing natural phenomenon?
      • You would expect 1/9 or 11%
    Digression begets its own digression This law holds so well in practice that it is used to catch forged data!! http://mathworld.wolfram.com/BenfordsLaw.html WHY? Iff there exists a universal distribution, it must be scale invariant (i.e., should work in any units)  starting from there we can show that the distribution must satisfy the differential eqn x P’(x) = -P(x) For which, the solution is P(x)=1/x ! 0.0791812 5 0.0457575 9 0.09691 4 0.0511525 8 0.124939 3 0.0579919 7 0.176091 2 0.0669468 6 0.30103 1                  
  • 64. 2/15 Review power laws Small-world phenomena in scale-free networks Link analysis for Web Applications
  • 65. Searchable Networks
    • Just because a short path exists, doesn’t mean you can easily find it.
    • You don’t know all of the people whom your friends know.
    • Under what conditions is a network searchable ?
    Kleinberg (2000)
  • 66. Neighborhood based random networks
    • Lattice is d -dimensional ( d =2).
    • One random link per node.
    • Probability that there is a link between two nodes u and v is r(u,v) - 
      • r(u,v) is the “lattice” distance between u and v (computed as manhattan distance)
        • As against geodesic or network distance computed in terms of number of edges
          • E.g. North-Rim and South-Rim
      •  determines how steeply the probability of links to far away neighbors reduces
    View of the world from 9 th Ave
  • 67. Searcheability in lattice networks
    • For d =2, dip in time-to-search at  =2
      • For low  , random graph; no “geographic” correlation in links
      • For high  , not a small world; no short paths to be found.
    • Searcheability dips at  =2 (inverse square distribution), in simulation
      • Corresponds to using greedy heuristic of sending message to the node with the least lattice distance to goal
    • For d-dimensional lattice, minimum occurs at  =d
  • 68. Searchable Networks
    • Watts, Dodds, Newman (2002) show that for d = 2 or 3, real networks are quite searchable.
    •  the dimensions are things like “geography”, “profession”, “hobbies”
    • Killworth and Bernard (1978) found that people tended to search their networks by d = 2: geography and profession.
    Kleinberg (2000) The Watts-Dodds-Newman model closely fitting a real-world experiment Ramin Zabih Kentaro Toyama
  • 69. ..but didn’t Milgram’s letter experiment show that navigation is easy?
    • … may be not
      • A large fraction of his test subjects were stockbrokers
        • So are likely to know how to reach the “goal” stockbroker
      • A large fraction of his test subjects were in boston
        • As was the “goal” stockbroker
      • A large fraction of letters never reached
        • Only 20% reached
    • So how about (re)doing Milgram experiment with emails?
      • People are even more burned out with (e)mails now
        • Success rate for chain completion < 1% !
  • 70. Credits Albert, Reka and A.-L. Barabasi. “Statistical mechanics of complex networks.” Reviews of Modern Physics , 74(1):47-94. (2002) Barabasi, Albert-Laszlo. Linked . Plume Publishing. (2003) Kleinberg, Jon M. “Navigation in a small world.” Science, 406:845. (2000) Watts, Duncan. Six Degrees: The Science of a Connected Age . W. W. Norton & Co. (2003)
  • 71. Six Degrees of Separation
    • The experiment:
    • Random people from Nebraska were to send a letter (via intermediaries) to a stock broker in Boston.
    • Could only send to someone with whom they were on a first-name basis.
    • Among the letters that found the target, the average number of links was six.
    Milgram (1967) Stanley Milgram (1933-1984)
  • 72. Outline
    • Small Worlds
    • Random Graphs
    • Alpha and Beta
    • Power Laws
    • Searchable Networks
    • Six Degrees of Separation
  • 73. Neighborhood based generative models These essentially give more links to close neighbors.. Discuss the powerlaw curve
  • 74. The Alpha Model
    • The people you know aren’t randomly chosen.
    • People tend to get to know those who are two links away (Rapoport * , 1957).
    • The real world exhibits a lot of clustering.
    Watts (1999) * Same Anatol Rapoport, known for TIT FOR TAT! The Personal Map by MSR Redmond’s Social Computing Group
  • 75. The Alpha Model
    •  model: Add edges to nodes, as in random graphs, but makes links more likely when two nodes have a common friend.
    • For a range of  values:
      • The world is small (average path length is short), and
      • Groups tend to form (high clustering coefficient).
    Watts (1999) Probability of linkage as a function of number of mutual friends (  is 0 in upper left, 1 in diagonal, and ∞ in bottom right curves.)
  • 76. The Alpha Model
    •  model: Add edges to nodes, as in random graphs, but makes links more likely when two nodes have a common friend.
    • For a range of  values:
      • The world is small (average path length is short), and
      • Groups tend to form (high clustering coefficient).
    Watts (1999)  Clustering coefficient / Normalized path length Clustering coefficient ( C ) and average path length ( L ) plotted against 
  • 77. The Beta Model Watts and Strogatz (1998)  = 0  = 0.125  = 1 People know others at random. Not clustered, but “small world” People know their neighbors, and a few distant people. Clustered and “ small world” People know their neighbors. Clustered, but not a “small world”
  • 78. The Beta Model
    • First five random links reduce the average path length of the network by half, regardless of N !
    • Both  and  models reproduce short-path results of random graphs, but also allow for clustering.
    • Small-world phenomena occur at threshold between order and chaos.
    Watts and Strogatz (1998) Nobuyuki Hanaki Jonathan Donner Kentaro Toyama Clustering coefficient / Normalized path length Clustering coefficient ( C ) and average path length ( L ) plotted against 