Lada Adamic HP Labs Palo Alto CA Talk

Lada Adamic, HP Labs, Palo Alto, CA

Talk outline Information flow through blogs Information flow through email Search through email networks Search within the enterprise Search in an online community

Implicit Structure and Dynamics of Blog. Space Eytan Adar, Li Zhang, Lada Adamic, & Rajan Lukose • Blog use: – Record real-world and virtual experiences – Note and discuss things “seen” on the net • Blog structure: blog-to-blog linking • Use + Structure – Great to track “memes” (catchy ideas)

Approaches and uses of blog analysis • Patterns of information flow – How does the popularity of a topic evolve over time? – Who is getting information from whom? • Ranking algorithms that take advantage of transmission patterns

Tracking popularity over time Slashdot Effect Popularity Boing Effect Time Blogdex, Blog. Pulse, etc. track the most popular links/phrases of the day

Different kinds of information have different popularity profiles 1 0. 9 Slashdot postings 0. 8 Major-news site (editorial content) – back of the paper Front-page news 0. 7 Products, etc. 0. 6 0. 5 0. 4 0. 3 0. 2 0. 1 0 5 10 15 5 % of hits received on each day since first appearance 10 15

Micro example: Giant Microbes

Microscale Dynamics • What do we need track specific info ‘epidemics’? – Timings – Underlying network b 2 b 1 b 3 t 0 Time of infection t 1

Microscale Dynamics • Challenges – Root may be unknown – Multiple possible paths – Uncrawled space, alternate media (email, voice) – No links bn b 2 ? b 1 ? b 3 t 0 Time of infection t 1

Microscale Dynamics who is getting info from whom • Explicit blog to blog links (easy) – Via links are even better • Implicit/Inferred transfer (harder) – Use ML algorithm for link inference problem • Support Vector Machine (SVM) • Logistic Regression – What we can use • • Full text Blogs in common Links in common History of infection

Visualization http: //www-idl. hp. com/blogstuff • Zoomgraph tool – Using Graph. Viz (by AT&T) layouts • Simple algorithm – If single, explicit link exists, draw it – Otherwise use ML algorithm • Pick the most likely explicit link • Pick the most likely possible link • Tool lets you zoom around space, control threshold, link types, etc.

Giant Microbes epidemic visualization via link explicit link inferred link blog

i. Rank Find early sources of good information using inferred information paths or timing b 3 b 4 b 1 True source b 2 Popular site b 5 … bn

i. Rank Algorithm • • Draw a weighted edge for all pairs of blogs that cite the same URL higher weight for mentions closer together run Page. Rank control for ‘spam’ t 0 Time of infection t 1

Do Bloggers Kill Kittens? 02: 00 AM Friday Mar. 05, 2004 PST Wired publishes: "Warning: Blogs Can Be Infectious. ” 7: 25 AM Friday Mar. 05, 2004 PST Slashdot posts: "Bloggers' Plagiarism Scientifically Proven" 9: 55 AM Friday Mar. 05, 2004 PST Metafilter announces "A good amount of bloggers are outright thieves. "

Information flow in social groups Fang Wu, Bernardo Huberman, Lada Adamic, Joshua Tyler

Spread of disease is affected by the underlying network co-worker mom college friend co-worker mike co-worker

Spread of computer viruses is affected by the underlying network co-worker mom college friend co-worker mike co-worker

Difference between information flow and disease/virus spread Viruses (computer and otherwise) are shared indiscriminately (involuntarily) Information is passed selectively from one host to another based on knowledge of the recipient’s interests

Spread of information is affected by its content, potential recipients, and network topology co-worker mom college friend co-worker mike co-worker

homophily: individuals with like interests associate with one another personal homepages at Stanford distance between personal homepages

The Model: Decay in transmission probability as a function of the distance m between potential target and originating node T(m) = (m+1)-b T power-law implies slowest decay m=2 m=0 m=1

P(k) Virus, information transmission on a scale free network outdegree k Degree distribution of all senders of email passing through the HP email server

epidemics on scale free graphs 106 nodes, epidemic if 1% (104) infected critical threshold 1 k=¥, b=0 k=100, b=1 0. 8 0. 6 0. 4 Wu et al. (2004) Newman (2002) 0. 2 0 1 Pastor-Satorras & Vespignani (2001) 1. 5 2 2. 5 a 3 3. 5 4

Study of the spread of URLs and attachments 40 participants (30 within HPL, 10 elsewhere in HP & other orgs) 6370 URLs and 3401 attachments crypotgraphically hashed Question: How many recipients in our sample did each item reach? caveats: messages are deleted (still, the median number of messages > 2000) non-uniform sample

Only forwarded messages are counted forwarded message forwarded URLs

Results number of items with so many recipients average = 1. 1 for attachments, and 1. 2 for URLs 10 10 4 3 email attachments -4. 1 x URLs -3. 6 x 2 ads at the bottom of hotmail & yahoo messages 1 0 10 10 number of recipients 1 short term expense control

Simulate transmission on email log each message has a probability p of transmitting information from an infected individual to the recipient 02/19/2003 15: 45: 33 I-1 I-2 02/19/2003 15: 45: 33 I-1 I-3 02/19/2003 15: 40 E-1 I-4 02/19/2003 15: 45: 52 I-5 E-2 02/19/2003 15: 45: 55 E-3 I-6 02/19/2003 15: 45: 58 I-7 I-8 02/19/2003 15: 46: 00 E-4 I-9 02/19/2003 15: 46: 05 I-10 I-11 02/19/2003 15: 46: 10 I-12 I-13 02/19/2003 15: 46: 10 I-12 I-14 02/19/2003 15: 46: 10 I-12 I-15 02/19/2003. . 15: 46: 14 I-16. . E-5. . internal node external node

Simulation of information transmission on the actual HP Labs email graph an individual is infected if they receive a particular piece of information individuals remain infected for 24 hours start by infecting one individual at random every time an infected individual sends an email they have a probability p of infecting the recipient track epidemic over the course of a week, most run their course in 1 -2 days

Introduce a decay in the transmission probability based on the hierarchical distance h. AB = 5 distance 2 distance 1 distance 2 B A distance 1

7119 potential recipients p 0

Conclusions on info flow in social groups Information spread typically does not reach epidemic proportions Information is passed on to individuals with matching properties The likelihood that properties match decreases with distance from the source Model gives a finite threshold Results are consistent with observed URL & attachment frequencies in a sample Simulations following real email patterns also consistent

How to search in a small world MA NE Milgram’s experiment: Given a target individual and a particular property, pass the message to a person you correspond with who is “closest” to the target.

Small world experiment at Columbia Dodds, Muhamad, Watts, Science 301, (2003) email experiement conducted in 2002 18 targets in 13 different countries 24, 163 message chains 384 reached their targets average path length 4. 0

Why study small world phenomena? Curiosity: Why is the world small? How are people able to route messages? Social Networking as a Business: Friendster, Orkut, My. Space Linked. In, Spoke, Visible. Path

Six degrees of separation - to be expected Pool and Kochen (1978) - average person has 500 -1500 acquaintances Ignoring clustering, other redundancy … ~ 103 first neighbors, 106 second neighbors, 109 third neighbors But networks are clustered: my friends’ friends tend to be my friends Watts & Strogatz (1998) - a few random links in an otherwise clustered graph give an average shortest path close to that of a random graph

But how are people are able to find short paths? How to choose among hundreds of acquaintances? Strategy: Simple greedy algorithm - each participant chooses correspondent who is closest to target with respect to the given property Models geography Kleinberg (2000) hierarchical groups Watts, Dodds, Newman (2001), Kleinberg(2001) high degree nodes Adamic, Puniyani, Lukose, Huberman (2001), Newman(2003)

Spatial search Kleinberg (2000) “The geographic movement of the [message] from Nebraska to Massachusetts is striking. There is a progressive closing in on the target area as each new person is added to the chain” S. Milgram ‘The small world problem’, Psychology Today 1, 61, 1967 nodes are placed on a lattice and connect to nearest neighbors additional links placed with f(d)~ d(u, v)-r if r = 2, can search in polylog (< (log. N)2) time

Kleinberg: searching hierarchical structures ‘Small-World Phenomena and the Dynamics of Information’, NIPS 14, 2001 Hierarchical network models: h is the distance between two individuals in hierarchy with branching b f(h) ~ b-ah If a = 1, can search in O(log n) steps Group structure models: q = size of smallest group that two individuals belong to f(q) ~ q-a If a = 1, can achieve in O(log n) steps

Identity and search in social networks Watts, Dodds, Newman (2001) individuals belong to hierarchically nested groups multiple independent hierarchies coexist pij ~ exp(-a x)

Identity and search in social networks Watts, Dodds, Newman (2001) There is an attrition rate r Network is ‘searchable’ if a fraction q of messages reach the target N=204800 N=409600 N=102400

High degree search Adamic et al. Phys. Rev. E, 64 46135 (2001) Mary Who could introduce me to Richard Gere? Bob Jane

power-law graph number of nodes found 94 67 63 54 2 6 1

Poisson graph number of nodes found 93 19 11 3 15 7 1

Scaling of search time with size of graph Sharp cutoff at k~N 1/a , 2 nd degree neighbors 3 covertime for half the nodes 10 random walk a = 0. 37 fit degree sequence a = 0. 24 fit 2 10 1 10 0 10 1 10 2 10 3 10 size of graph 4 10 5 10

Testing the models on social networks (w/ Eytan Adar) Use a well defined network: HP Labs email correspondence over 3. 5 months Edges are between individuals who sent at least 6 email messages each way Node properties specified: degree geographical location position in organizational hierarchy Can greedy strategies work?

Strategy 1: High degree search Degree distribution of all senders of email passing through the HP email server outdegree

Filtered network (6 messages sent each way) Degree distribution no longer power-law, but Poisson 450 users median degree = 10 mean degree = 13 average shortest path = 3 High degree search performance (poor): median # steps = 16 mean = 40

Strategy 2: Geography

Communication across corporate geography 1 U 1 L 87 % of the 4000 links are between individuals on the same floor 4 U 2 U 3 U 2 L 3 L

Cubicle distance vs. probability of being linked optimum for search

Finding someone in a sea of cubicles median = 7 mean = 12

Strategy 3: Organizational hierarchy

Email correspondence scrambled

Actual email correspondence

Example of search path distance 2 distance 1 hierarchical distance = 5 search path distance = 4

Probability of linking vs. distance in hierarchy in the ‘searchable’ regime: 0 < a < 2 (Watts 2001)

Results distance search geodesic org random median 4 3 6 28 mean 5. 7 (4. 7) 3. 1 6. 1 57. 4

Group size vs. probability of linking

Group size and probability of linking optimum for search (Kleinberg 2001) group size g

Search Conclusions Individuals associate on different levels into groups. Group structure facilitates decentralized search using social ties. HP Labs as a social network is searchable but not quite optimal. searching using the organizational hierarchy is faster than using physical location A fraction of ‘important’ individuals are easily findable Humans may be much more resourceful in executing search tasks: making use of weak ties using more sophisticated strategies

People. Finder 2 – a search engine for HP people Extract & disambiguate names from publicly available documents Enrich information available about individuals Search for them by topic Identify knowledge communities from co-occurrence of names Live Demo If live demo fails: Current People. Finder functionality People. Finder 2 info on a person Extracted topics for a person Social network visualization Search for individuals by topic Visualize knowledge network Find social network paths to experts

To find out more: (papers, slides, other research in the group) Information dynamics group (IDL) at HP Labs: http: //www. hpl. hp. com/research/idl List of publications http: //www. hpl. hp. com/personal/Lada_Adamic/research. html