基于图卷积神经网络的多视角聚类

针对多视角数据间互补与一致特性难以刻画问题,提出一种基于图卷积神经网络的多视角聚类方法。通过对样本不同视角间相同邻接子图基于图卷积神经网络学习到的表达进行约束,有效挖掘了多视角数据间的一致特性。通过共享图卷积神经网络参数、学习不同视角完整邻接图嵌入表达并串接得到多视角表达,有效挖掘了多视角数据间的互补特性。对上述多视角表达增加相对熵约束,使得最终学习到的多视角表达得以提升并符合聚类特性。在五个数据集上均取得了最好的聚类效果,说明所提出的基于图卷积神经网络的聚类方法可以有效挖掘视角间互补与一致特性并提升聚类性能。     

一种支持结构化P2P网络性能优化的双向克隆节点模型

In order to reduce the maintenance cost of structured Peer-to-Peer (P2P), Clone Node Protocol (CNP) based on user behavior is proposed. CNP considers the regularity of user behavior and uses the method of clone node. A Bidirectional Clone Node Chord model (BCNChord) based on CNP protocol is designed and realized. In BCNChord, Anticlockwise Searching Algorithm, Difference Push Synchronize Algorithm and Optimal Maintenance Algorithm are put forward to increase the performances. In experiments, according to the frequency of nodes, the maintenance cost of BCNChord can be 3.5% ~32.5% lower than that of Chord. In the network of 212 nodes, the logic path hop is steady at 6, which is much more prior to 12 of Chord and 10 of CNChord. Theoretical analysis and experimental results show that BCNChord can effectively reduce the maintenance cost of its structure and simultaneously improve the query efficiency up to (1/4)O (logN). BCNChord is more suitable for highly dynamic environment and higher real-time system.