1. 1 Social networks Small world networks

2. TU/e - 0ZM05/0EM15/0A150 2 Course aim knowledge about concepts in network theory, and being able to apply that knowledge

3. TU/e - 0ZM05/0EM15/0A150 3 The setup in some more detail Network theory and background - Introduction: what are they, why important … - Small world networks - Four basic network arguments - Kinds of network data (collection) - Business networks

4. TU/e - 0ZM05/0EM15/0A150 Two approaches to network theory Bottom up (let’s try to understand network characteristics and arguments) as in … “Four network arguments” by Matzat (lecture 3) Top down (let’s have a look at many networks, and try to deduce what is happening from what we see) as in “small world networks” (now) 4

5. TU/e - 0ZM05/0EM15/0A150 What kind of structures do networks have, empirically? Answer: often “small-world”, and often also scale-free 5

6. TU/e - 0ZM05/0EM15/0A150 3 important network properties Average Path Length (APL) ( ) Shortest path between two nodes i and j of a network, averaged across all (pairs of) nodes Clustering coefficient (“cliquishness”) Number of closed triplets / Total number of triplets (or: probability that two of my ties are connected) (Shape of the) degree distribution A distribution is “scale free” when P(k), the proportion of nodes with degree k follows this formula, for some value of gamma: 6

7. TU/e - 0ZM05/0EM15/0A150 7 Example 1 - Small world networks NOTE - Edge of network theory - Not fully understood yet … - … but interesting findings

8. TU/e - 0ZM05/0EM15/0A150 Enter: Stanley Milgram (1933-1984) Remember him? 8

9. TU/e - 0ZM05/0EM15/0A150 9 The small world phenomenon – Milgram´s (1967) original study Milgram sent packages to several (60? 160?) people in Nebraska and Kansas. Aim was “get this package to ” Rule: only send this package to someone whom you know on a first name basis. Aim: try to make the chain as short as possible. Result: average length of a chain is only six “six degrees of separation”

10. TU/e - 0ZM05/0EM15/0A150 10 Milgram’s original study (2) An urban myth?  Milgram used only part of the data, actually mainly the ones supporting his claim  Many packages did not end up at the Boston address  Follow up studies typically small scale

11. TU/e - 0ZM05/0EM15/0A150 11 The small world phenomenon (cont.) “Small world project” has been testing this assertion (not anymore, see http://smallworld.columbia.edu)http://smallworld.columbia.edu Email to, otherwise same rules. Addresses were American college professor, Indian technology consultant, Estonian archival inspector, … Conclusion:  Low completion rate (384 out of 24,163 = 1.5%)  Succesful chains more often through professional ties  Succesful chains more often through weak ties (weak ties mentioned about 10% more often)  Chain size 5, 6 or 7.

12. TU/e - 0ZM05/0EM15/0A150 Some Milgram follow-ups… 12 6.6!

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14. TU/e - 0ZM05/0EM15/0A150 14 The Kevin Bacon experiment – Tjaden ( +/- 1996) Actors = actors Ties = “has played in a movie with”

15. TU/e - 0ZM05/0EM15/0A150 15 The Kevin Bacon game Can be played at: http://oracleofbacon.org Kevin Bacon number (data might have changed by now) Jack Nicholson:1 (A few good men) Robert de Niro:1 (Sleepers) Rutger Hauer (NL):2 [Nick Stahl] Famke Janssen (NL):2 [Nick Stahl] Bruce Willis:2 [Patrick Michael Strange] Kl.M. Brandauer (AU):2 [Robert Redford] Arn. Schwarzenegger:2 [Kevin Pollak]

16. TU/e - 0ZM05/0EM15/0A150 16 A search for high Kevin Bacon numbers… 3 2

17. TU/e - 0ZM05/0EM15/0A150 17 The center of the movie universe (sept 2013) Nr 370 Nr 136 Nr 39

18. TU/e - 0ZM05/0EM15/0A150 The best centers… (2013 + 2011) 18 (Kevin Bacon at place 444 in 2011) (Rutger Hauer at place 39, J.Krabbé 935)

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20. TU/e - 0ZM05/0EM15/0A150 20 “Elvis has left the building …”

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22. TU/e - 0ZM05/0EM15/0A150 22 We find small average path lengths in all kinds of places… Caenorhabditis Elegans 959 cells Genome sequenced 1998 Nervous system mapped  low average path length + cliquishness = small world network Power grid network of Western States 5,000 power plants with high-voltage lines  low average path length + cliquishness = small world network

23. TU/e - 0ZM05/0EM15/0A150 How weird is that? 23

24. TU/e - 0ZM05/0EM15/0A150 Could there be a simple explanation? Consider a random network: each pair of nodes is connected with a given probability p. This is called an Erdos-Renyi network. 24 NB Erdos was a “Kevin Bacon” long before Kevin Bacon himself!|

25. TU/e - 0ZM05/0EM15/0A150 APL is small in random networks 25 [Slide copied from Jari_Chennai2010.pdf]

26. TU/e - 0ZM05/0EM15/0A150 26 [Slide copied from Jari_Chennai2010.pdf]

27. TU/e - 0ZM05/0EM15/0A150 But let’s move on to the second network characteristic … 27

28. TU/e - 0ZM05/0EM15/0A150 28

29. TU/e - 0ZM05/0EM15/0A150 This is how small-world networks are defined: A short Average Path Length and A high clustering coefficient … and a randomly “grown” network does NOT lead to these small-world properties 29

30. TU/e - 0ZM05/0EM15/0A150 Networks of the Real-world (1) Information networks:  World Wide Web: hyperlinks  Citation networks  Blog networks Social networks: people + interactions  Organizational networks  Communication networks  Collaboration networks  Sexual networks  Collaboration networks Technological networks:  Power grid  Airline, road, river networks  Telephone networks  Internet  Autonomous systems Florence families Karate club network Collaboration network Friendship network Source: Leskovec & Faloutsos

31. TU/e - 0ZM05/0EM15/0A150 Networks of the Real-world (2) Biological networks  metabolic networks  food web  neural networks  gene regulatory networks Language networks  Semantic networks Software networks … Yeast protein interactions Semantic network Language network Software network Source: Leskovec & Faloutsos

32. TU/e - 0ZM05/0EM15/0A150 32 Small world networks … so what? You see it a lot around us: for instance in road maps, food chains, electric power grids, metabolite processing networks, neural networks, telephone call graphs and social influence networks  may be useful to study them They seem to be useful for a lot of things, and there are reasons to believe they might be useful for innovation purposes (and hence we might want to create them)

33. TU/e - 0ZM05/0EM15/0A150 Examples of interesting properties of small world networks 33

34. TU/e - 0ZM05/0EM15/0A150 Synchronizing fireflies … Synchronization speed depends on small-world properties of the network  Network characteristics important for “integrating local nodes” 34

35. TU/e - 0ZM05/0EM15/0A150 35 Combining game theory and networks – Axelrod (1980), Watts & Strogatz (1998 ? ) 1. Consider a given network. 2. All connected actors play the repeated Prisoner’s Dilemma for some rounds 3. After a given number of rounds, the strategies “reproduce” in the sense that the proportion of the more succesful strategies increases in the network, whereas the less succesful strategies decrease or die 4. Repeat 2 and 3 until a stable state is reached. 5. Conclusion: to sustain cooperation, you need a short average distance, and cliquishness (“small worlds”)

36. TU/e - 0ZM05/0EM15/0A150 36 And another peculiarity... Seems to be useful in “decentralized computing”  Imagine a ring of 1,000 lightbulbs  Each is on or off  Each bulb looks at three neighbors left and right... ... and decides somehow whether or not to switch to on or off. Question: how can we design a rule so that the network can tackle a given GLOBAL (binary) question, for instance the question whether most of the lightbulbs were initially on or off. - As yet unsolved. Best rule gives 82 % correct. - But: on small-world networks, a simple majority rule gets 88% correct. How can local knowledge be used to solve global problems?

37. TU/e - 0ZM05/0EM15/0A150 If small-world networks are so interesting and we see them everywhere, how do they arise? (potential answer: through random rewiring of a given structure) 37

38. TU/e - 0ZM05/0EM15/0A150 38 Strogatz and Watts 6 billion nodes on a circle Each connected to nearest 1,000 neighbors Start rewiring links randomly Calculate average path length and clustering as the network starts to change Network changes from structured to random APL: starts at 3 million, decreases to 4 (!) Clustering: starts at 0.75, decreases to zero (actually to 1 in 6 million) Strogatz and Watts asked: what happens along the way with APL and Clustering?

39. TU/e - 0ZM05/0EM15/0A150 39 Strogatz and Watts (2) “We move in tight circles yet we are all bound together by remarkably short chains” (Strogatz, 2003)  Implications for, for instance, research on the spread of diseases... The general hint: -If networks start from relatively structured … -… and tend to progress sort of randomly … -- … then you might get small world networks a large part of the time

40. TU/e - 0ZM05/0EM15/0A150 And now the third characteristic 40

41. TU/e - 0ZM05/0EM15/0A150 And if we consider all three… 41

42. TU/e - 0ZM05/0EM15/0A150 … then we find this: 42 Wang & Chen (2003) Complex networks: Small-world, Scale-free and beyond

43. TU/e - 0ZM05/0EM15/0A150 43 Same thing … we see “scale-freeness” all over

44. TU/e - 0ZM05/0EM15/0A150 … and it can’t be based on an ER-network 44

45. TU/e - 0ZM05/0EM15/0A150 45

46. TU/e - 0ZM05/0EM15/0A150 46 Scale-free networks are: Robust to random problems/mistakes...... but vulnerable to selectively targeted attacks

47. TU/e - 0ZM05/0EM15/0A150 47 Another BIG question: How do scale free networks arise? Potential answer: Perhaps through “preferential attachment” (Another) critique to this approach: it ignores ties created by those in the network

48. TU/e - 0ZM05/0EM15/0A150 Some related issues 48

49. TU/e - 0ZM05/0EM15/0A150 49 “The tipping point” (Watts*) Consider a network in which each node determines whether or not to adopt, based on what his direct connections do. Nodes have different thresholds to adopt (randomly distributed) Question: when do you get cascades of adoption? Answer: two phase transitions or tipping points:  in sparse networks no cascades, as networks get more dense you get cascades suddenly  as networks get more heterogenous, a sudden jump in the likelihood of cascades  as networks get even more heterogenous, the likelihood of cascades decreases * Watts, D.J. (2002) A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences USA 99, 5766-5771

50. TU/e - 0ZM05/0EM15/0A150 50 “Find the influentials” (or not?)

51. TU/e - 0ZM05/0EM15/0A150 51 Malcolm Gladwell (journalist/writer: wrote “Blink” and “The tipping point” Duncan Watts (scientist, Yahoo, Microsoft Research) http://www.youtube.com/watch?v=AtnR5 H6AVVU http://www.fastcompany.com/641124 /tipping-point-toast

52. TU/e - 0ZM05/0EM15/0A150 52 "All they'll ever say," Watts insists, is that a) there are people who are more influential than others, and b) they are disproportionately important in getting a trend going. That may be oversimplifying it a bit, but last year, Watts decided to put the whole idea to the test by building another Sims-like computer simulation. He programmed a group of 10,000 people, all governed by a few simple interpersonal rules. Each was able to communicate with anyone nearby. With every contact, each had a small probability of "infecting" another. And each person also paid attention to what was happening around him: If lots of other people were adopting a trend, he would be more likely to join, and vice versa. The "people" in the virtual society had varying amounts of sociability--some were more connected than others. Watts designated the top 10% most-connected as Influentials; they could affect four times as many people as the average Joe. In essence, it was a virtual society run--in a very crude fashion--according to the rules laid out by thinkers like Gladwell and Keller. Watts set the test in motion by randomly picking one person as a trendsetter, then sat back to see if the trend would spread. He did so thousands of times in a row. The results were deeply counterintuitive. The experiment did produce several hundred societywide infections. But in the large majority of cases, the cascade began with an average Joe (although in cases where an Influential touched off the trend, it spread much further). To stack the deck in favor of Influentials, Watts changed the simulation, making them 10 times more connected. Now they could infect 40 times more people than the average citizen (and again, when they kicked off a cascade, it was substantially larger). But the rank-and-file citizen was still far more likely to start a contagion. Why didn't the Influentials wield more power? With 40 times the reach of a normal person, why couldn't they kick-start a trend every time? Watts believes this is because a trend's success depends not on the person who starts it, but on how susceptible the society is overall to the trend--not how persuasive the early adopter is, but whether everyone else is easily persuaded. And in fact, when Watts tweaked his model to increase everyone's odds of being infected, the number of trends skyrocketed. "If society is ready to embrace a trend, almost anyone can start one--and if it isn't, then almost no one can," Watts concludes. To succeed with a new product, it's less a matter of finding the perfect hipster to infect and more a matter of gauging the public's mood. Sure, there'll always be a first mover in a trend. But since she generally stumbles into that role by chance, she is, in Watts's terminology, an "accidental Influential."

53. TU/e - 0ZM05/0EM15/0A150 Hmm... We will see that you do not always end up with small worlds! 53

54. TU/e - 0ZM05/0EM15/0A150 54 The bigger picture: Understanding macro patterns from micro behavior

55. TU/e - 0ZM05/0EM15/0A150 The general approach … understand how STRUCTURE can arise from underlying MICRO-DYNAMICS Scientists are trying to connect the structural properties … Scale-free, small-world, locally clustered, bow-tie, hubs and authorities, communities, bipartite cores, network motifs, highly optimized tolerance, … … to processes (Erdos-Renyi) Random graphs, Exponential random graphs, Small-world model, Preferential attachment, Edge copying model, Community guided attachment, Forest fire models, Kronecker graphs, …

56. TU/e - 0ZM05/0EM15/0A150 56 RECAP Six degrees of separation: average path length is often small Many real-world networks have properties that are similar: small-world and scale-free. We do not really understand yet how these properties emerge. We saw two clues: the Watts-Strogatz model for small-worlds and the preferential attachment model for scale-freeness. Small-world and scale-free networks have some nice properties (which might explain why they exist) Considerable controversy over what these kinds of results imply, for instance for marketing purposes Hot scientific topic: connecting micro-behavior to macro-properties

57. TU/e - 0ZM05/0EM15/0A150 57 To Do: Read and comprehend the papers on small world networks, scale-free networks (see website, there is extra material too). Think about implications and applications of these results