1. Microsoft Instant Messenger Communication Network How does the world communicate? Jure Leskovec (jure@cs.cmu.edu) Machine Learning Department http://www.cs.cmu.edu/~jure Joint work with: Eric Horvitz, Microsoft Research

2. Networks: Why?  Today: large on-line systems leave detailed records of social activity  On-line communities: MyScace, Facebook  Email, blogging, instant messaging  On-line publications repositories, arXiv, MedLine  Emerging behavior (need lots of data):  Actions of individual nodes are independent but global patterns and regularities emerge

3. The Largest Social Network  What is the largest social network in the world (that we can relatively easily obtain)? For the first time we had a chance to look at complete (anonymized) communication of the whole planet (using Microsoft MSN instant messenger network) 3

4. Instant Messaging Contact (buddy) list Messaging window 4

5. Instant Messaging as a Network 5 Buddy Conversation

6. IM – Phenomena at planetary scale Observe social phenomena at planetary scale:  How does communication change with user demographics (distance, age, sex)?  How does geography affect communication?  What is the structure of the communication network? 6

7. Communication data The record of communication  Presence data  user status events (login, status change)  Communication data  who talks to whom  Demographics data  user age, sex, location 7

8. Data description: Presence  Events:  Login, Logout  Is this first ever login  Add/Remove/Block buddy  Add unregistered buddy (invite new user)  Change of status (busy, away, BRB, Idle,…)  For each event:  User Id  Time 8

9. Data description: Communication  For every conversation (session) we have a list of users who participated in the conversation  There can be multiple people per conversation  For each conversation and each user:  User Id  Time Joined  Time Left  Number of Messages Sent  Number of Messages Received 9

10. Data description: Demographics  For every user (self reported):  Age  Gender  Location (Country, ZIP)  Language  IP address (we can do reverse geo IP lookup) 10

11. Data collection  Log size: 150Gb/day  Just copying over the network takes 8 to 10h  Parsing and processing takes another 4 to 6h  After parsing and compressing ~ 45 Gb/day  Collected data for 30 days of June 2006:  Total: 1.3Tb of compressed data 11

12. Network: Conversations 12 Conversation

13. Data statistics Activity over June 2006 (30 days)  245 million users logged in  180 million users engaged in conversations  17,5 million new accounts activated  More than 30 billion conversations 13

14. Data statistics per day Activity on June 1 2006  1 billion conversations  93 million users login  65 million different users talk (exchange messages)  1.5 million invitations for new accounts sent 14

15. User characteristics: age 15

16. Age piramid: MSN vs. the world 16

17. Conversation: Who talks to whom?  Cross gender edges:  300 male-male and 235 female-female edges  640 million female-male edges 17

18. Number of people per conversation  Max number of people simultaneously talking is 20, but conversation can have more people 18

19. Conversation duration  Most conversations are short 19

20. Conversations: number of messages Sessions between fewer people run out of steam 20

21. Time between conversations  Individuals are highly diverse  What is probability to login into the system after t minutes?  Power-law with exponent 1.5  Task queuing model [Barabasi]  My email, Darvin’s and Einstein’s letters follow the same pattern 21

22. Age: Number of conversations User self reported age High Low 22

23. Age: Total conversation duration User self reported age High Low 23

24. Age: Messages per conversation User self reported age High Low 24

25. Age: Messages per unit time User self reported age High Low 25

26. Who talks to whom: Number of conversations 26

27. Who talks to whom: Conversation duration 27

28. Geography and communication  Count the number of users logging in from particular location on the earth 28

29. How is Europe talking  Logins from Europe 29

30. Users per geo location Blue circles have more than 1 million logins. 30

31. Users per capita Fraction of population using MSN: Iceland: 35% Spain: 28% Netherlands, Canada, Sweden, Norway: 26% France, UK: 18% USA, Brazil: 8% Fraction of population using MSN: Iceland: 35% Spain: 28% Netherlands, Canada, Sweden, Norway: 26% France, UK: 18% USA, Brazil: 8% 31

32. Communication heat map  For each conversation between geo points (A,B) we increase the intensity on the line between A and B 32

33.  Correlation:  Probability: Homophily (gliha v kup štriha) Age vs. Age 33

34. Per country statistics  On a particular typical day… 34 Country# of logins# of users# of messagesMessages per user USA38,319,36313,261,337412,729,27831.12 Brazil20,582,6137,864,424467,972,52259.50 France19,163,1316,475,858518,931,78580.13 Unknown18,444,3526,872,347191,167,08527.81 Spain16,868,5496,140,895503,759,24082.03 UK16,659,0095,724,826487,018,47085.07 Canada14,558,6925,021,185160,249,68631.91 China14,225,1635,314,463101,003,72919.00 Turkey13,619,7894,696,555353,540,47575.27 Mexico10,756,9894,359,932209,195,10047.98 Note that global usage and market share statistics are higher if we accumulate data over longer time periods.

35. Per typical user per country  On a typical day MSN user from a country … 35 Country Logins on a particular day Users on a particular day Messages sent Messages per user Slovenia364,988130,88415,919,892121.6335992 Malta122,84641,8294,993,316119.3745009 Hungary1,214,268427,32047,623,604111.4471684 Bosnia105,58435,6893,254,17091.18131637 Teunion100,33533,3993,041,63591.0696428 Gibraltar19,0966,452581,19590.07982021 UK16,659,0095,724,826487,018,47085.07131396 Macedonia126,72943,7543,669,97783.87751977 Netherlands7,399,1602,696,669221,300,21082.06428375 Spain16,868,5496,140,895503,759,24082.03352117 Note that global usage and market share numbers are higher if we accumulate data over longer time periods.

36. What about Slovenia (per capita)? StatisticNumber Rank (per capita) Conversations inside19,868,88622 Conversation to outside7,868,48348 Total conversations27,737,36929 Avg. time inside309.49147 Avg. time outside314.3980 Avg. time inside (pct.)0.4960 Messages sent inside9.7832 Messages sent outside9.4619 Messages inside (pct.)0.5083 36

37. Who is Slovenia talking to? 37 Rank Target Country Pairs of people Number of conversations Avg. time per conv. Avg. # of messages 1Slovenia13,41,25019,868,886309.49.78 2USA61,794922,527303.49.14 3Spain27,650310,357289.47.97 4UK14,709204,335325.49.02 5Germany9,047129,551350.310.20 6Bosnia9,956114,509385.914.62 7Yugoslavia8,194104,270381.712.55 8Italy8,612100,698358.89.89 9Croatia6,83884,362359.011.00 10Turkey10,76377,651292.48.08 11Albania9,51776,440320.710.88 12Sweden5,08369,019306.98.34 13Netherlands5,06168,287315.98.87 14Canada5,00360,617301.87.38

38. Instant Messaging as a Network 38 Buddy

39. IM Communication Network  Buddy graph:  240 million people (people that login in June ’06)  9.1 billion edges (friendship links)  Communication graph:  There is an edge if the users exchanged at least one message in June 2006  180 million people  1.3 billion edges  30 billion conversations 39

40. Buddy network: Number of buddies  Buddy graph: 240 million nodes, 9.1 billion edges (~40 buddies per user) 40

41. Communication Network: Degree  Number of people a users talks to in a month 41

42. Network: Small-world  6 degrees of separation [Milgram ’60s]  Average distance 5.5  90% of nodes can be reached in < 8 hops HopsNodes 110 278 3396 48648 53299252 628395849 779059497 852995778 910321008 101955007 11518410 12149945 1344616 1413740 154476 161542 17536 18167 1971 2029 2116 2210 233 242 253

43. Network: Searchability  Milgram’s experiment showed:  (1) short paths exist in networks  (2) humans are able to find them  Assume the following setting:  Nodes are scattered on a plane  Given starting node u and we want to reach target node v  Algorithm: always navigate to a neighbor that is geographically closest to target node v  Surprise: Geo-routing finds the short paths (for appropriate distance measure) 43 u v

44. Communication network: Clustering  How many triangles are closed?  Clustering normally decays as k -1  Communication network is highly clustered: k -0.37 High clusteringLow clustering 44

45. Communication Network Connectivity 45

46. k-Cores decomposition  What is the structure of the core of the network? 46

47. k-Cores: core of the network  People with k<20 are the periphery  Core is composed of 79 people, each having 68 edges among them 47

48. Network robustness  We delete nodes (in some order) and observe how network falls apart:  Number of edges deleted  Size of largest connected component 48

49. Robustness: Nodes vs. Edges 49

50. Robustness: Connectivity 50

51. Conclusion  A first look at planetary scale social network  The largest social network analyzed  Strong presence of homophily: people that communicate share attributes  Well connected: in only few hops one can research most of the network  Very robust: Many (random) people can be removed and the network is still connected 51

52. References  Leskovec and Horvitz: Worldwide Buzz: Planetary-Scale Views on an Instant- Messaging Network, 2007  http://www.cs.cmu.edu/~jure http://www.cs.cmu.edu/~jure 52