distributed advice-seeking on an evolving social network

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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. - PowerPoint PPT Presentation


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Distributed Advice-Seeking on an Evolving Social NetworkDept Computer Science and Software Engineering The University of Melbourne - Australia

Golriz RezaeiJens Pfau Michael Kirley

IAT10 Conference Sep 2010 Toronto York University

1Overview Context (Advice Seeking + Evolving Social Network) Abstract framework Related work Details of our model Evaluation by experiments Discussion & Conclusion Questions2

Distributed Infrastructure Technology Ex./ Specialized protein search engines, Netflix

CharacteristicsUnknown large environmentVarieties of selection optionsHeterogeneous usersCharacteristics 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

Context Advice Seeking

Question?3Context Advice SeekingQuestion:

Heterogeneous individual requirements Whom?

Challenge: Identify other suitable users difficult!

Social Networks! - Large number of them - Preferences not publicly available - Not in a position to make their own preferences explicit4Context Evolving Social NetworksImportant role many real-world & multi-agent systems

Typical objectives:

Real-world [Gross and Blasius 2008] :(co-evolution)

Significant studies evolutionary game theory [Szab and Fth 2007]

Social contacts serve as resourcesmanage improve long term payoff gains - describing networks topology - understanding system behaviour as a function of topologyBehaviourTopologyAgentsstrategiesNetworksstructure5Agent-based simulation (resources + agents)


Subjective Utility Goal = maximize long term utility, limited selectionsChallenge = 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


Abstract Framework

This capability

Connection network Advice exchange

Agents interactions Social relationships

The evolving social network Utility gain

What we study?

Affect the match?How co-evolve?Improve?Change?Related work Distributed Recommender SystemsNo 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) + Trust relationships (keep track of accuracy of recommendationsVidal 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

Overview of the proposed modelAlgorithm: Evolving Social Network Advice seekingRequire: Population of agents , set of resources , number of rounds , evolutionary rate , maximum out degree , recommendation threshold t, default edge weight1: Weighted Graph = INITIALIZE GRAPH ( , , )2: for r = 1 to do3: for each a in random order do4:5:if RANDOM() > then6:ACCESS RESOURCE (a, )7:else8:Query (a, , , t)9:end if 10:if RANDOM() < then 11:ADAPT LINKS (a, , RANDOM() < , ) 12:end if 13: end for 14: end for

1-Initialization2-Exploitation/Exploration3-Advice selection4-Assessment *5-Network Adaptation *

Heterogeneous pool of resourcesn-dimensional binary feature vector fr initialized randomly

Heterogeneous agent population n-dimensional binary preference vector pa initialized randomly2 scenarios: random agents no structural restrictionsocial agents outgoing edges, default weight (0.5)Steps of the model1-Initialization

Select based on personal knowledge / Query others!

Probabilistic richness of the agents acquired knowledgeExploit access the largest utility resource it knows so for far

Explore seek advice (resource, utility) Random agents other random agentssocial agents outgoing edges, social contacts

Steps of the model cont.2-Exploitation/Exploration

A suggestion probabilisticallyAdvisor links weightOne of his suggestions reported utility

Subjective utility of accessed resourceSimilarity between pa & fr Normalized Hamming distance mapped to [-1,1]

Positive values better than average random selectionNegative values random selection would have done better

Steps of the model cont.3-Advice selection

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!

Steps of the model cont.4-Assessment *

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]Steps of the model cont.5-Network Adaptation *Last 2 steps eventually make link with similar preferencesSimilar-minded community spot beneficial resources fasterSnapshots of the model

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 trialsExperiment 1 Basic model behaviourSocial agents gain higher utilities? (|A| = 100, |pa| & |fr| = 3, |R| = 5000)

17Experiment 2 The influence of environmental complexityEfficiency of social and random scenarios facing more complex environments?|A| = 100|pa| & |fr| = 3|R| = (1000, 5000, 10000, 50000)

18Experiment 3 Analysing the underlying networkCo-evolution systems behavior + structural properties

Modularity distinct communities of the network [Leicht and Newman 2008]

|A| = (100 , 300)|R| = 5000|pa| & |fr| = (2, 3, 4, 5)19Experiment 3 Analysing the underlying network cont.

Small populationLarge population20Discussion & ConclusionResults 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 utility21Questions?Thank you!22ReferencesC. 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 3749, 2003T. Gross and B. Blasius. Adaptive coevolutionary networks: A review. Journal of the Royal Society Interface, 5(20):259271, 2008E. A. Leicht and M. E. J. Newman. Community structure in directed networks. Physical Review Letters, 100(11):118703, 2008P. Massa and P. Avesani. Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems, pages 1724, 2007L. 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 10841085, 2003G. Szab and G. Fth. Evolutionary games on graphs. Physics Reports, 446(4-6):97216, 2007J. 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, 2005F. 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):5774, 2008

23Experiment 3 The influence of heterogeneityFinding similar-minded agents important roleHow heterogeneityin |pa| & |fr| affect the performance of social agents?|A| = (100 , 300)|R| = 5000|pa| & |fr| = (2, 3, 4, 5)T = 1000Averaged accumulated utility

24MetricsAverage utility

Average error rate











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