Emergence and Evolution of Coalitions in Buyer-Seller Networks

Walter, Frank E.; Battiston, Stefano; Schweitzer, Frank

doi:10.1007/978-3-540-71075-2_19

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…systems, web-based selling systems, bookmark web-sites, and so on. Motivated by the significance in economy and society, recently, the design of an efficient recommendation algorithm becomes a joint focus from marketing practice [45,46] to mathematical analysis [47], from engineering science [48,49,50] to physics community [51,52,53]. Basically, a recommendation system consists of users and objects, and each user has collected some objects.…”

Section: Personal Recommendationmentioning

confidence: 99%

How to project a bipartite network?

Zhou,

Ren,

Medo

et al. 2007

Preprint

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The one-mode projecting is extensively used to compress the bipartite networks. Since the onemode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the networkbased resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method in compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do personal recommendation?

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Section: Personal Recommendationmentioning

confidence: 99%

How to project a bipartite network?

Zhou,

Ren,

Medo

et al. 2007

Preprint

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“…This paper was inspired by ideas in [21] and is an extension of a paper presented at the Fifth International Workshop on Emergent Intelligence on Networked Agents (WEIN 2010) at the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010).…”

Section: Introductionmentioning

confidence: 99%

Trust as the Basis of Coalition Formation in Electronic Marketplaces

Walter

2011

Advs. Complex Syst.

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Despite the fact that social networks are ubiquitous on the Internet, only few websites exploit the potential of combining user communities and online marketplaces. Not many platforms allow users to engage in a phenomenon called "group buying" — buyers joining groups, or coalitions, to bundle their purchasing power towards sellers. We argue that this may be due to a lack of face-to-face interaction on the Internet; often, users do not know which other users to trust, which makes them suspicious of engaging in online business, in particular if many unknown other parties are involved. This situation, however, can be alleviated by leveraging the social networks of users: based on who a user knows and is connected to, a trust metric — for example, the TrustWebRank metric developed by us — can be computed to assess who else may be considered trustworthy to that user. In this paper, we build a simple agent-based model of coalition formation among agents in the setting of group buying in an electronic marketplace. In this model, agents use their trust relationships in order to determine who to form coalitions with. We show that this leads agents to experience high utility and that agents are able to learn who is trustworthy and who is not, even when they have no initial knowledge about the trustworthiness of other agents. This work may provide the foundation for a real-world application of an online coalition formation platform for e-commerce built on a social networking platform such as Facebook.

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