基于资源流行度的对等网络统计特征分析

对等网络体现出丰富的结构特征,如何深入认识更为精细的统计特征有待于进一步探索. 文章通过定义资源流行度阈值,建立基于资源流行度阈值的用户网络,体现对等网络中精细的结构特征. 针对一个具体的对等网络研究发现,基于低流行度资源形成的用户网络具备更加明晰的用户集群特性:随着资源流行度阈值的增大,分簇特征更为明显,且各簇内用户兴趣趋同性增强,不同簇间用户兴趣取向差异增大,用户分簇准确性提高. 更进一步,从各簇内用户的共享资源中提取基于资源粒度的低维簇指纹,该簇指纹可以在维度较低的情况下提供较高的表征精度. 关键词： 对等网络 流行度阈值 簇结构 簇指纹

Popularity based network statistical analysis in peer-to-peer application

There are rich statistical characteristics in a peer-to-peer (p2p) network. The more refined statistical characteristics still need further understanding. In this paper we define the popularity threshold of the resource, and abstract the user network based on the popularity threshold to reflect the refined structure characteristics. Through the emprical study of a workload from a dominant peer-to-peer file sharing system, we confirm that the user network based on the popularity threshold has more clear cluster features than the original network. With the popularity threshold of resource increasing, the clustering is more evident. The homoplasy of users within the same cluster is enhanced. The clustering accuracy is inproved. Furthermore, in this paper we extract the cluster fingerprints which can provide a high representation accuracy in low dimensions.