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Distributed Advice-Seeking on an Evolving Social Network Dept Computer Science and Software Engineering The University of Melbourne - Australia Golriz.

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Presentation on theme: "Distributed Advice-Seeking on an Evolving Social Network Dept Computer Science and Software Engineering The University of Melbourne - Australia Golriz."— Presentation transcript:

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 Distributed Infrastructure Technology Ex./ Specialized protein search engines, Netflix Characteristics 1)Unknown large environment 2)Varieties of selection options 3)Heterogeneous users 4)Characteristics not available until accessed, if it is made explicit at all Approaches 1) Individual try & error 2) Central registration directory (web service [Facciorusso et. al. 2003] ) 3) Advice seeking Direct exchange of “selection advice” beneficial! ex./ Learning [ Nunes and Oliveira 2003 ], distributed recommender systems Context Advice Seeking Question?

4 Context Advice Seeking Question: 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 explicit

5 Context Evolving Social Networks 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 BehaviourTopology Agents’ strategies Network’s structure

6 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? Abstract Framework

7 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?

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) + Trust relationships (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: for each 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 for 14: end for 1-Initialization 2-Exploitation/Exploration 3-Advice selection 4-Assessment * 5-Network Adaptation *

10 Heterogeneous pool of resources n-dimensional binary feature vector f r initialized randomly Heterogeneous agent population n-dimensional binary preference vector p a initialized randomly 2 scenarios: – random agents no structural restriction – social agents outgoing edges, default weight (0.5) Steps of the model 1-Initialization

11 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 Explore seek advice (resource, utility) Random agents other random agents social agents outgoing edges, social contacts Steps of the model cont. 2-Exploitation/Exploration

12 A suggestion probabilistically 1. Advisor link’s weight 2. One of his suggestions reported utility Subjective utility of accessed resource – Similarity between p a & f r – Normalized Hamming distance mapped to [-1,1] Positive values better than average random selection Negative values random selection would have done better Steps of the model cont. 3-Advice selection

13 Social agents learn from their interactions adjust the weight of links Following a particular suggestion - Positive | u a (r) – u rep (r)| < thr dis - N egative Adjust the link weight with multiple advisors - the link weight - w(a,b) < thr tolerance remove the edge, free slot! Steps of the model cont. 4-Assessment *

14 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 *

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

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 |p a | & |f r | = (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, |p a | & |f r | = 3, |R| = 5000)

18 Experiment 2 The influence of environmental complexity Efficiency of social and random scenarios facing more complex environments? |A| = 100 |p a | & |f r | = 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 |p a | & |f r | = (2, 3, 4, 5)

20 Experiment 3 Analysing the underlying network cont. Small population Large 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 |p a | & |f r | affect the performance of social agents? |A| = (100, 300) |R| = 5000 |p a | & |f r | = (2, 3, 4, 5) T = 1000 Averaged accumulated utility

25 Metrics Average utility Average error rate Efficiency

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