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Issue No. 03 - March (2013 vol. 24)
ISSN: 1045-9219
pp: 601-613
Eva Jaho , National and Kapodistrian University of Athens, Athens
Merkourios Karaliopoulos , National and Kapodistrian University of Athens, Athens
Ioannis Stavrakakis , National and Kapodistrian University of Athens, Athens
This paper explores how the degree of similarity within a social group can dictate the behavior of the individual nodes, so as to best tradeoff the individual with the social benefit. More specifically, we investigate the impact of social similarity on the effectiveness of content placement and dissemination. We consider three schemes that represent well the spectrum of behavior-shaped content storage strategies: the selfish, the self-aware cooperative, and the optimally altruistic ones. Our study shows that when the social group is tight (high degree of similarity), the optimally altruistic behavior yields the best performance for both the entire group (by definition) and the individual nodes (contrary to typical expectations). When the group is made up of members with almost no similarity, altruism or cooperation cannot bring much benefit to either the group or the individuals and thus, selfish behavior emerges as the preferable choice due to its simplicity. Notably, from a theoretical point of view, our “similarity favors cooperation” argument is inline with sociological interpretations of human altruistic behavior. On a more practical note, the self-aware cooperative behavior could be adopted as an easy to implement distributed alternative to the optimally altruistic one; it has close to the optimal performance for tight social groups and the additional advantage of not allowing mistreatment of any node, i.e., its induced content retrieval cost is always smaller than the cost of the selfish strategy.
Measurement, Games, Aggregates, Shape, Humans, Social network services, Computational complexity, social groups, Content replication, cooperation, similarity

M. Karaliopoulos, E. Jaho and I. Stavrakakis, "Social Similarity Favors Cooperation: The Distributed Content Replication Case," in IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 601-613, 2013.
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