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Distributed Advice-Seeking on an Evolving Social Network

Distributed Advice-Seeking on an Evolving Social Network. Dept Computer Science and Software Engineering The University of Melbourne - Australia Golriz Rezaei Jens Pfau Michael Kirley IAT10 Conference Sep 2010 – Toronto York University. Overview.

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Distributed Advice-Seeking on an Evolving Social Network

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  1. Distributed Advice-Seeking on an Evolving Social Network Dept Computer Science and Software Engineering The University of Melbourne - Australia Golriz Rezaei Jens Pfau Michael Kirley IAT10 Conference Sep 2010 – Toronto York University

  2. Overview • Context (Advice Seeking + Evolving Social Network) • Abstract framework • Related work • Details of our model • Evaluation by experiments • Discussion & Conclusion • Questions

  3. Context Advice Seeking • Distributed Infrastructure Technology Ex./Specialized protein search engines, Netflix • Characteristics • Unknown large environment • Varieties of selection options • Heterogeneous users • Characteristics not available • until accessed, if it is made explicit at all • Approaches • Individual try & error • Central registration directory (web service [Facciorusso et. al. 2003]) • Advice seeking Direct exchange of “selection advice” beneficial! • ex./ Learning [Nunes and Oliveira 2003 ], distributed recommender systems Question?

  4. Context Advice Seeking • Question: • Heterogeneousindividual requirements Whom? • Challenge:Identify other suitable users difficult! - Large number of them - Preferences not publicly available - Not in a position to make their own preferences explicit Social Networks!

  5. Context Evolving Social Networks Topology Behaviour • Important role many real-world & multi-agent systems • Typical objectives: • Real-world [Gross and Blasius 2008] : (co-evolution) • Significant studies evolutionary game theory [Szabó and Fáth 2007] Social contacts serve as resources manage improve long term payoff gains - describing network’s topology - understanding system behaviour as a function of topology Network’s structure Agents’ strategies

  6. Abstract Framework • Agent-based simulation (resources + agents) • Repeatedly • Subjective Utility • Goal = maximize long term utility, limited selections • Challenge = identify appropriate resources • Evolving Social Network - Autonomously, based on local information only make connections similar minded - Receive advice improve resource selection - Learn their own subjective utility advice accuracy decide retain/drop the contact - Seek referrals make new connections Match?

  7. What we study? • This capability • Connection network Advice exchange • Agents’ interactions Social relationships • The evolving social network Utility gain Affect the match? How co-evolve? Change? Improve?

  8. Related work Distributed Recommender Systems • No central authority Users exchange recommendations directly [Golbeck 2005,Massa 2007] • Need to find the right contacts to link to • Walter et. Al. 2008: Social networking (fixed, random) + Trustrelationships (keep track of accuracy of recommendations • Vidal 2005: Different model, engaging in a dyadic exchange rational choice both agents believe they share similar interests • Difference to our work - Implicit underlying network structure not used only keep track of the received advice - Do not optimize their position connect with those similar minded

  9. Overview of the proposed model Algorithm: Evolving Social Network Advice seeking Require:Population of agents , set of resources , number of rounds , evolutionary rate , maximum out degree , recommendation threshold t, default edge weight 1: Weighted Graph = INITIALIZE GRAPH ( , , ) 2: for r = 1 to do 3: foreach a∈ in random order do 4: 5:if RANDOM() > then 6: ACCESS RESOURCE (a, ) 7: else 8: Query (a, , , t) 9: end if 10: if RANDOM() < then 11: ADAPT LINKS(a, , RANDOM() < , ) 12: end if 13:end for14: end for 1-Initialization 2-Exploitation/Exploration 4-Assessment * 3-Advice selection 5-Network Adaptation *

  10. Steps of the model 1-Initialization • Heterogeneous pool of resources n-dimensional binary feature vector frinitialized randomly • Heterogeneous agent population n-dimensional binary preference vector painitialized randomly • 2 scenarios: • random agents no structural restriction • social agents outgoing edges, default weight (0.5)

  11. Steps of the model cont. 2-Exploitation/Exploration • Select based on personal knowledge / Query others! • Probabilistic richness of the agent’s acquired knowledge • Exploit access the largest utility resource it knows so for far • Exploreseek advice (resource, utility) Random agents other random agents social agentsoutgoing edges, social contacts

  12. Steps of the model cont. 3-Advice selection • A suggestion probabilistically • Advisor link’s weight • One of his suggestions reported utility • Subjective utility of accessed resource • Similarity between pa & fr • Normalized Hamming distance mapped to [-1,1] Positive values better than average random selection Negative values random selectionwould have done better

  13. Steps of the model cont. 4-Assessment * • Social agents learn from their interactions adjust the weight of links • Following a particular suggestion - Positive | ua (r) – urep (r)| < thrdis - Negative • Adjust the link weight with multiple advisors - the link weight - w(a,b) < thrtolerance remove the edge, free slot!

  14. Steps of the model cont. 5-Network Adaptation * • Social agents opportunity to change their links probabilistically! • Link to a random agent with default weight • Ask for referrals trust propagation [Massa and Avesani 2007, Vidal 2005]

  15. Snapshots of the model • Last 2 steps eventually make link with similar preferences • Similar-minded community spot beneficial resources faster

  16. Experimental Evaluation & Setup • Monte-Carlo simulations, various parameter settings • Scenarios (social agents only and random agents only) • Population sizes (small = 100, large = 300 agents) • Environmental complexity |R| = (1000, 5000, 10000, 50000) • Heterogeneity |pa| & |fr| = (2, 3, 4, and 5) • First 1000 iterations (note! exhaustive exploration will find eventually) • Average over 30 independent trials

  17. Experiment 1 Basic model behaviour • Social agents gain higher utilities? (|A| = 100, |pa| & |fr| =3, |R| = 5000)

  18. Experiment 2 The influence of environmental complexity • Efficiency of social and random scenarios facing more complex environments? |A| = 100 |pa| & |fr| =3 |R| = (1000, 5000, 10000, 50000)

  19. Experiment 3 Analysing the underlying network • Co-evolution system’s behavior + structural properties • Modularity distinct communities of the network [Leicht and Newman 2008] |A| = (100 , 300) |R| = 5000 |pa| & |fr| = (2, 3, 4, 5)

  20. Experiment 3 Analysing the underlying network cont. Large population Small population

  21. Discussion & Conclusion • Results strongly connected communities with similar preferences • Lead to higher utility especially during the initial period (still unaware about their subjectively “best” resources) • significant outcome small personal knowledge of the resource pool • Interesting implications development/operation of concrete systems • Small average path length ( < 6) few link adaptations • Recognize communities autonomously cater for their specific needs • Level of heterogeneity (agents/resources) affects the gained utility

  22. Questions? Thank you!

  23. References • C. Facciorusso, S. Field, R. Hauser, Y. Hoffner, R. Humbel, R. Pawlitzek, W. Rjaibi, and C. Siminitz. A web services matchmaking engine for web services. In E-Commerce and Web Technologies, Lecture Notes in Computer Science, pages 37–49, 2003 • T. Gross and B. Blasius. Adaptive coevolutionary networks: A review. Journal of the Royal Society Interface, 5(20):259–271, 2008 • E. A. Leicht and M. E. J. Newman. Community structure in directed networks. Physical Review Letters, 100(11):118703, 2008 • P. Massa and P. Avesani. Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems, pages 17–24, 2007 • L. Nunes and E. Oliveira. Advice-exchange in heterogeneous groups of learning agents. In Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pages 1084–1085, 2003 • G. Szabó and G. Fáth. Evolutionary games on graphs. Physics Reports, 446(4-6):97–216, 2007 • J. M. Vidal. A protocol for a distributed recommender system. In J. Sabater R. Falcone, S. Barber and M. Singh, editors, Trusting Agents for Trusting Electronic Societies. Springer, 2005 • F. E. Walter, S. Battiston, and F. Schweitzer. A model of a trust-based recommendation system on a social network. Autonomous Agents and Multi-Agent Systems, 16(1):57–74, 2008

  24. Experiment 3 The influence of heterogeneity • Finding similar-minded agents important role • How heterogeneity in |pa| & |fr| affect the performance of social agents? |A| = (100 , 300) |R| = 5000 |pa| & |fr| = (2, 3, 4, 5) T = 1000 Averaged accumulated utility

  25. Metrics • Average utility • Average error rate • Efficiency

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