Distributed Advice-Seeking on an Evolving Social Network
Dept Computer Science and Software Engineering The University of Melbourne - Australia
Golriz RezaeiJens Pfau
Michael Kirley
IAT10 Conference
Sep 2010 – Toronto York University
Overview
Context (Advice Seeking + Evolving Social Network) Abstract framework Related work Details of our model Evaluation by experiments Discussion & Conclusion Questions
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Distributed Infrastructure Technology Ex./ Specialized protein search engines, Netflix
Characteristics1) Unknown large environment2) Varieties of selection options3) Heterogeneous users4) Characteristics not available
until accessed, if it is made explicit at all
Approaches1) Individual try & error2) 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
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Question?
Context Advice Seeking
Question:
Heterogeneous individual requirements Whom?
Challenge: Identify other suitable users difficult!
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Social Networks!
- Large number of them - Preferences not publicly available - Not in a position to make their own preferences explicit
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 resourcesmanage improve long term payoff gains
- describing network’s topology - understanding system behaviour as a function of topology
BehaviourTopology
Agents’
strategies
Network’s
structure
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
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This capability
Connection network Advice exchange
Agents’ interactions Social relationships
The evolving social network Utility gain
What we study?
UnknownAffect the match?
How co-evolve?
Improve?
Change?
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
Overview of the proposed model
Algorithm: 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-Initialization
2-Exploitation/Exploration
3-Advice selection 4-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 randomly
2 scenarios: – random agents no structural restriction– social agents outgoing edges, default weight (0.5)
Steps of the model1-Initialization
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
A suggestion probabilistically1. Advisor link’s weight
2. 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 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 preferences Similar-minded community spot beneficial resources faster
Snapshots 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 trials
Experiment 1 Basic model behaviour
Social agents gain higher utilities? (|A| = 100, |pa| & |fr| = 3, |R| = 5000)
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)
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)
Experiment 3 Analysing the underlying network cont.
Small population Large population
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
Questions?
Thank you!
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
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 = 1000Averaged accumulated utility
Metrics
Average utility
Average error rate
Efficiency
