Using multiagent self-organization techniques for seeking information in virtual social communities

Annabelle Mercier, M. Occello, Jean-Paul Jamont
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引用次数: 2

Abstract

This paper proposes an approach which aims at improving information access skills, services in large networks. It proposes a self-organized multiagent analysis of the problem allowing to reduce the number of messages transmitted for a skill search. The MWAC Multi-Wireless-Agent Communication model is extended to take some specificities of social networks into account, like the information held by the social network members and their connections with the others.The network of agents may represent a social network. Each agent holds information about members agents and is linked with other agents which represent its neighborhood. Each member builds a local network representation according to its needs. A global view of a social network is not dynamically available and furthermore not desirable. Our approach is thus based on a decentralized interrogation of the network and on organizational structures detection.With this method, a search does not need to flood all the network with the query because it uses the self-organized structure that emerges so, it can avoid a saturation in the network and it reduces the amount of transmitted messages. In order to show the interest of the approach, our proposition is validated by several simulations. The applications of this work are related to skill searching in a virtual social network.
利用多智能体自组织技术在虚拟社会群体中搜索信息
本文提出了一种旨在提高大型网络中信息访问技能和服务的方法。它提出了一个自组织的多智能体分析问题,允许减少为技能搜索传输的消息数量。对MWAC多无线代理通信模型进行了扩展,使其考虑到社会网络的一些特性,如社会网络成员所持有的信息及其与其他成员的联系。代理网络可以代表一个社会网络。每个代理持有成员代理的信息,并与代表其邻居的其他代理链接。每个成员根据自己的需要构建一个本地网络表示。社会网络的全局视图不是动态可用的,而且也不可取。因此,我们的方法是基于对网络的分散询问和组织结构检测。使用这种方法,搜索不需要用查询淹没整个网络,因为它使用了出现的自组织结构,因此可以避免网络饱和并减少传输的消息量。为了显示该方法的有效性,我们的建议通过几个仿真验证。本研究的应用与虚拟社交网络中的技能搜索有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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