1. An Ant Colony Optimization Approach Expert Identification in Social Networks Muhammad Aurangzeb Ahmad, Jaideep Srivastava Department of Computer Science and Engineering, University of Minnesota International Workshop on Social Computing, Behavioral Modeling, and Prediction Phoenix, AZ, April 1-2, 2008

2. Outline  Part A: Minnesota research program  Data mining @ Minnesota  SBP research @ Minnesota  Part B: Expert identification in social networks  Background  Possible Approaches  Ant Colony Optimization (ACO)  ACO for Expert Identification  Current Work  Results  Future Work University of Minnesota

3. Data Mining at Minnesota Core Research Models, algorithms tools NSF, ARL, NASA Business Applications Sales & marketing (IBM) Automotive (Daimler/Chrysler) CRM (Intel) e-CRM (Intuit) Claims fraud (Ingenix/UHG) Bio-Medical Applications Medical informatics (Mayo) Bioinformatics (NIH) Behavioral ecology (NSF) Government Applications Cyber security (ARDA, ARL) Transportation (MnDoT, FHWA) Physical security (United Tech) Tax Audit (Dept of Revenue) Driver modeling (Eaton) Science & Engineering Applications Climate modeling (NASA) High energy physics (Fermi) Penetration mechanics (ARL) Simulation (LLNL, DOE) Vehicle Health (NASA)  Technology transfer  Minnesota Intrusion Detection System (MINDS) – being used by Army Research Lab and University of Minnesota  Sales Opportunity Miner – IBM is building a full-fledged tool based on this research  Global Climate Modeling for NASA  MN Dept of Revenue uses our models for selecting tax payers to audit

4. Books Authored by DM Faculty

5. SBP Research @ Minnesota  Virtual world exploratorium for computational social sciences  Computational approach to modeling trust  Trust, reputation, social capital, etc.  SNA driven healthcare management incentive design  Structure and resource identification from the Web  Information search, expert identification, community identification, etc.

6. MMO Games  MMO (Massively Multiplayer Online) Games are computer games that allow hundreds to thousands of players to interact and play together in a persistent online world Popular MMO Games- Everquest 2, World of Warcraft and Second Life

7. University of Minnesota 7 Sponsorship National Science Foundation Army Research Institute Sony Corporation UMN, UIUC, USC, Northwestern Sociology research questions How do networks within the ecosystem of groups enable and constrain the formation of groups? How do micro-group processes influence group effectiveness and social identity? Psychology research questions What impact does playing video games has people’s real lives? Is online behavior different in MMOs vs. tradition video games? Macroeconomics research questions lots of them Computer Science research questions quantitative metrics algorithms & scalability Marketing questions (Sony) early identification of customer attrition social influence and its impact on up-sell & cross-sell Team training what team structures and communication patterns facilitate and/or inhibit performance how can +ve structures & patterns be incorporated in training programs Enabling research in multiple disciplines Noshir Contractor, Northwestern communication theory, orgn theory Scott Poole, Urbana sociology Dmitri Williams, USC social psychology Jaideep Srivastava, Minnesota computer science Sony Corporation gaming, marketing, scalability Key resource Everquest 2 dataset from Sony 250,000+ players 3+ years complete click-stream 14+ terabytes of data

8. Building a Web of Trust w/o Trust Ratings 1 A framework for deriving degree of trust The relationship between a review writer and a review rater 1-1: Calculating Quality of a Review and Reputation of a Review Rater 1-2: Calculating Reputation of a Review Writer 1-3: Constructing Users_Category Expertise Matrix E 1. Young Ae Kim, Hady W. Lauw, Ee-Peng Lim, Jaideep Srivastava, Building a Web of Trust without Explicit Trust Ratings, ICDE 2008 Workshop.

9. University of Minnesota Discovering Referral Networks from Medicare Data Pulmono logist Cardio logist Geriatrics Podiatrist Rheumato logist Medical Problems Patient Doug Wholey, Healthcare Policy & Management Dave Knutson, Minnesota Department of Health Jaideep Srivastava, Computer Science & Engineering

10. University of Minnesota Referral Networks and Cooperation  Problem  In many cases people visit multiple doctors and specialists for their medical needs  The patients would be served better if there were better coordination between these specialists  Classical approach  Offer incentives individually to specialists  Defects in this approach  Each specialist may want to “optimize” his/her own incentives  In such settings local optimization of services does not lead to global optimization of services  Proposed approach  Identify Referral Networks to encourage specialists to work together to offer better services  provide group incentives

11. Outline  Part A: Minnesota research program  Data mining @ Minnesota  SBP research @ Minnesota  Part B: Expert identification in social networks  Background  Possible Approaches  Ant Colony Optimization (ACO)  ACO for Expert Identification  Current Work  Results  Future Work University of Minnesota

12. Problem Background  Problem: Expert Identification in Social Networks  Setting:  A Dynamically Changing Social Network  A Dynamic Distribution of Topics.  No central management.  Reduce response time, Avoid flooding.  Problem Formulation:  Given a graph of E experts, a topic distribution T, devise an algorithm for expert identification that can be incrementally updated. University of Minnesota

13. Possible Approaches  Have a centralized repository of expertise and experts.  Assumes that one already knows what the 'topics' are and who the corresponding experts are.  Alternatively maintain a topic hierarchy over the network.  Also assumes that the topics and that the topics are stationary. University of Minnesota

14. ACO (Ant Colony Optimization) ‏  Initial Conditions:  A colony of ants foraging for food.  No central ‘brain’ controlling the ants.  Foraging for Food (Resource)  Initially ants set off in random directions to forage for food.  When an ant finds a food source it retraces its path.  Ants lay chemical trials called pheromones in their path which can evaporate if not reinforced. University of Minnesota

15. ACO (Ant Colony Optimization) ‏  Frequently used path become reinforced while the less frequently used paths become weak.  Ants follow the paths which have stronger trails. University of Minnesota

16. An ACO Model for Expert Identification  Queries are represented as ants.  Whenever a query ant finds an answer to a query it retraces its path and lays out a trail  Forward Ant and Backward Ant  Experts are the nodes with strong trails leading to them.  The first c ants are just allowed to traverse the network like a k-random walker.  Time to Live: maximum number of iterations that the ant should explore the network if the answer to the query is not found. University of Minnesota

17. ACO Approach  Queries are routed based on the scents.  Multiple keywords as different types of scents.  Different pheromones are combined for cases where one encounters an unfamiliar query.  The network as consisting of multiple types of pheromone trails. University of Minnesota

18. The ACO Approach  Amount of Pheromone Laid Q = The length of the path, j = edge, i = ant Lj = Distance from the origin to the node under consideration  Route Selection (multiple keywords) U = set of neighbors of the current node F Q = set of already visited nodes j = node to be selected University of Minnesota

19. Experiments and Results  ACO approach vs. K-random walker.  This is analogous to the situation where one does not know who the experts are.  Evaluation Metrics (Adopted from Michalmyr)  Resource Usage: Number of edges traversed for each query within a given period of time.  Hit rate: Number of queries satisfied within a given period of time.  Efficiency: Resource usage / Hit rate. University of Minnesota

20. Experiments University of Minnesota Size of Network = 10,000

21. Conclusion  A 'solution' that self-organizes.  'Solution' can be incrementally built.  Graceful degradation of performance.  Can account for changes in the network.  Topics for expertise do not have to be predefined.  An ant colony optimization approach for expert identification.  Topic based and key word based approach. University of Minnesota

22. Appendix: Related Work (ACO)  The Any Colony Optimization (ACO) Algorithm was developed by Margo Dorigo in 1992.  Main Applications: Assignment Problems, Scheduling Problems, Routing Problems.  ACO is ideal for problems where minimal cost has to be computed.  SemAnt, Query Routing in distributed environment with a predefined taxonomy. (ElkeMichlmayr) University of Minnesota

23. Appendix: Related Work  Expert Identification  Text on Message Boards. (ContactFinder)  E-mail and text analysis. (Schwartz et al.)  Graph Based Ranking Approaches. (Campell)  Query Routing  Broadcasting, Flooding.  History Based Query Routing. (Cohen et al.) REMINDIN (Tempich et al.)  Kleingberg’s Query Incentive Networks University of Minnesota