Social Network Analysis talk at UT Austin

1. Social Network Analysis at
Linkedin
Mitul Tiwari
Search, Network, and Analytics (SNA)
LinkedIn
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2. Who am I?
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3. Outline
About Me
About LinkedIn
Analytics products at LinkedIn
Fun Facts: Sequencing the Startup DNA
3

4. LinkedIn by the numbers
120,000,000+ users (August 4, 2011)
2+ new user registrations per second
81+ Million monthly unique users* (Comscore)
2 Billion People Searches in 2010
7.1 Billion page views in Q2 2010
2+ Million companies with LinkedIn Company Pages
2+ M LinkedIn Groups
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5. Broad Range of Products
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6. User Profile
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7. Connections
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8. Communications
Communications
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9. Hiring Solutions
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10. Job Search
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11. Company Pages
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12. Outline
About Me
About LinkedIn
Analytics products at LinkedIn
Fun Facts: Sequencing the Startup DNA
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13. What do I mean by Analytics Products?
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14. People You May Know
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15. Profile Stats: WVMP
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16. Viewers of this profile also ...
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17. Skills
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18. InMaps
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19. Related Searches
Millions of Searches everyday
Help users to explore and refine their queries
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20. Analytics Products: Key Ideas
Recommendations
People You May Know, Related Searches, Viewers of
this profile ...
Insight
Profile Stats: Who Viewed My Profile, Skills
Visualization
InMaps
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21. Analytics Products: Challenges
LinkedIn: largest professional network
120+ million members on LinkedIn
Billions of pageviews
Terabytes of data to process
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22. Outline
Analytics products at LinkedIn
Deep Dive - Connection Strength
Deep Dive - Related Searches
Fun Facts: Sequencing the Startup DNA
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23. Connection Strength
Let’s build “Connection Strength”
Systems and Tools we use
Hadoop MapReduce
Managing workflow
Serving data in production
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24. Connection Strength
How well do people know each other?
Connection Strength
Application: reorder updates to show updates
from close connections
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25. Connection Strength
How well do
Alice
people know each
other?
Bob Carol
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26. Connection Strength
How well do
Alice
people know each
other?
Bob Carol
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27. Connection Strength
How well do
Alice
people know each
other?
Bob Carol
Triangle closing
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28. Connection Strength
How well do
Alice
people know each
other?
Bob Carol
Triangle closing
Prob(Bob knows Carol) ~ the # of common connections
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29. Triangle Closing in Pig
-- connections in (source_id, dest_id) format in both directions
connections = LOAD `connections` USING PigStorage();
group_conn = GROUP connections BY source_id;
pairs = FOREACH group_conn GENERATE
generatePair(connections.dest_id) as (id1, id2);
common_conn = GROUP pairs BY (id1, id2);
common_conn = FOREACH common_conn GENERATE
flatten(group) as (source_id, dest_id),
COUNT(pairs) as common_connections;
STORE common_conn INTO `common_conn` USING PigStorage();
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30. Pig Overview
Load: load data, specify format
Store: store data, specify format
Foreach, Generate: Projections, similar to select
Group by: group by column(s)
Join, Filter, Limit, Order, ...
User Defined Functions (UDFs)
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31. Triangle Closing in Pig
-- connections in (source_id, dest_id) format in both directions
connections = LOAD `connections` USING PigStorage();
group_conn = GROUP connections BY source_id;
pairs = FOREACH group_conn GENERATE
generatePair(connections.dest_id) as (id1, id2);
common_conn = GROUP pairs BY (id1, id2);
common_conn = FOREACH common_conn GENERATE
flatten(group) as (source_id, dest_id),
COUNT(pairs) as common_connections;
STORE common_conn INTO `common_conn` USING PigStorage();
31

32. Triangle Closing in Pig
-- connections in (source_id, dest_id) format in both directions
connections = LOAD `connections` USING PigStorage();
group_conn = GROUP connections BY source_id;
pairs = FOREACH group_conn GENERATE
generatePair(connections.dest_id) as (id1, id2);
common_conn = GROUP pairs BY (id1, id2);
common_conn = FOREACH common_conn GENERATE
flatten(group) as (source_id, dest_id),
COUNT(pairs) as common_connections;
STORE common_conn INTO `common_conn` USING PigStorage();
32

33. Triangle Closing in Pig
-- connections in (source_id, dest_id) format in both directions
connections = LOAD `connections` USING PigStorage();
group_conn = GROUP connections BY source_id;
pairs = FOREACH group_conn GENERATE
generatePair(connections.dest_id) as (id1, id2);
common_conn = GROUP pairs BY (id1, id2);
common_conn = FOREACH common_conn GENERATE
flatten(group) as (source_id, dest_id),
COUNT(pairs) as common_connections;
STORE common_conn INTO `common_conn` USING PigStorage();
33

34. Triangle Closing in Pig
-- connections in (source_id, dest_id) format in both directions
connections = LOAD `connections` USING PigStorage();
group_conn = GROUP connections BY source_id;
pairs = FOREACH group_conn GENERATE
generatePair(connections.dest_id) as (id1, id2);
common_conn = GROUP pairs BY (id1, id2);
common_conn = FOREACH common_conn GENERATE
flatten(group) as (source_id, dest_id),
COUNT(pairs) as common_connections;
STORE common_conn INTO `common_conn` USING PigStorage();
34

35. Triangle Closing in Pig
-- connections in (source_id, dest_id) format in both directions
connections = LOAD `connections` USING PigStorage();
group_conn = GROUP connections BY source_id;
pairs = FOREACH group_conn GENERATE
generatePair(connections.dest_id) as (id1, id2);
common_conn = GROUP pairs BY (id1, id2);
common_conn = FOREACH common_conn GENERATE
flatten(group) as (source_id, dest_id),
COUNT(pairs) as common_connections;
STORE common_conn INTO `common_conn` USING PigStorage();
35

36. Triangle Closing Example
Alice
Bob Carol
connections = LOAD `connections` USING
1.(A,B),(B,A),(A,C),(C,A) PigStorage();
2.(A,{B,C}),(B,{A}),(C,{A})
3.(A,{B,C}),(A,{C,B})
4.(B,C,1), (C,B,1)
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37. Triangle Closing Example
Alice
Bob Carol
1.(A,B),(B,A),(A,C),(C,A)
group_conn = GROUP connections BY
2.(A,{B,C}),(B,{A}),(C,{A}) source_id;
3.(A,{B,C}),(A,{C,B})
4.(B,C,1), (C,B,1)
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38. Triangle Closing Example
Alice
Bob Carol
1.(A,B),(B,A),(A,C),(C,A)
2.(A,{B,C}),(B,{A}),(C,{A})
pairs = FOREACH group_conn GENERATE
3.(A,{B,C}),(A,{C,B}) generatePair(connections.dest_id) as (id1, id2);
4.(B,C,1), (C,B,1)
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39. Triangle Closing Example
Alice
Bob Carol
1.(A,B),(B,A),(A,C),(C,A)
2.(A,{B,C}),(B,{A}),(C,{A}) common_conn = GROUP pairs BY (id1, id2);
common_conn = FOREACH common_conn
3.(A,{B,C}),(A,{C,B}) GENERATE flatten(group) as (source_id, dest_id),
4.(B,C,1), (C,B,1) COUNT(pairs) as common_connections;
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40. Connection Strength
Age
Company Overlap
School Overlap
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41. Related Searches
Let’s build “Related Searches”
Systems and Tools we use
Hadoop MapReduce
Managing workflow
Serving data in production
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42. Systems and Tools
Kafka (LinkedIn)
Hadoop (Apache)
Azkaban (LinkedIn)
Voldemort (LinkedIn)
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43. Data Cycle
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44. Systems and Tools
Kafka
publish-subscribe messaging system
transfer data from production to HDFS
Hadoop
Azkaban
Voldemort
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45. Systems and Tools
Kafka
Hadoop
Java MapReduce and Pig
process data
Azkaban
Voldemort
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46. Systems and Tools
Kafka
Hadoop
Azkaban
Hadoop workflow management tool
to manage hundreds of Hadoop jobs
Voldemort
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47. Systems and Tools
Kafka
Hadoop
Azkaban
Voldemort
Key-value store
store output of Hadoop jobs and serve in production
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48. Related Searches
Let’s build “Related Searches”
Systems and Tools we use
Hadoop MapReduce
Managing workflow
Serving data in production
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49. Hadoop MapReduce
Large-scale distributed data processing
Map phase: partition the problem in smaller
steps
Reduce phase: aggregate the output of smaller
steps
Allows parallelism and fault-tolerance
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50. Related Searches
Let’s build “Related Searches”
Systems and Tools we use
Hadoop MapReduce
Managing workflow
Serving data in production
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51. Our Workflow
Triangle
Age School Overlap
Closing
Union
push-to-prod
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52. Our Workflow
Triangle
Age School Overlap
Closing
Union
push-to-qa push-to-prod
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53. Sample Workflow
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54. Azkaban Workflow Management
Dependency management
Regular Scheduling
Monitoring
Diverse jobs: Java, Pig, Clojure
Configuration/Parameters
Resource control/locking
Restart/Stop/Retry
Visualization
History
Logs
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55. Our Workflow
Triangle
Age School Overlap
Closing
Union
push-to-prod
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56. Our Workflow
Triangle
Age School Overlap
Closing
Union
push-to-prod
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57. Related Searches
Let’s build “Related Searches”
Systems and Tools we use
Hadoop MapReduce
Managing workflow
Serving data in production
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58. Voldemort
Large amount of data/Scalable
Quick lookup/low latency
Versioning and Rollback
Fault tolerance through replication
Read only
Offline index building
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59. Voldemort RO Store
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60. Outline
Analytics products at LinkedIn
Deep Dive - Connection Strength
Deep Dive - Related Searches
Fun Facts: Sequencing the Startup DNA
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61. Related Searches
Millions of Searches everyday
Help users to explore and refine their queries
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62. How to build Related Searches?
Collaborative Filtering: searches done in the
same session
Q1 Q2 Q3 Q4
Time
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63. How to build Related Searches?
Searches correlated by results clicks
Q1 R1
Qn Rm
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64. How to build Related Searches?
Searches with overlapping terms
Q1 Jeff LinkedIn
Q2 LinkedIn CEO
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65. Outline
Analytics products at LinkedIn
Deep Dive - Connection Strength
Deep Dive - Related Searches
Fun Facts: Sequencing the Startup DNA
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66. Sequencing the Startup DNA
Done by Monica Rogati at
LinkedIn
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67. Sequencing the Startup DNA
•Top majors: Entrepreneurship, Computer Engineering
•Bottom: Nursing, Administration
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68. Sequencing the Startup DNA
•Most founders age at first startup between 20 and 40
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69. Sequencing the Startup DNA
•Top regions: San Francisco, NYC
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70. Sequencing the Startup DNA
•Top Business Schools:
Stanford, Harvard, MIT Sloan
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71. Things Covered
Analytics products at LinkedIn
Deep Dive - Connection Strength
Deep Dive - Related Searches
Fun Facts: Sequencing the Startup DNA
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72. SNA Team
Thanks to SNA Team at LinkedIn
http://sna-projects.com
We are hiring!
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73. Questions?
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