Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start link prediction is favored as one of the most valuable subproblems of traditional link prediction. However, due to the loss of many links in the observation network, the topological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topological information from observed network becomes the key point to solve the problem of cold-start link prediction. In this paper, we propose a framework for solving the cold-start link prediction problem, a joint-weighted symmetric nonnegative matrix factorization model fusing graph regularization information, based on low-rank approximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designed graph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain each other. Finally, a unified framework for implementing cold-start link prediction is constructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validation on five real networks with attributes shows that the proposed model has very good predictive performance when predicting missing edges of isolated nodes.     

实现Excel和Matlab的数据通讯方法的研究与应用

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基于动态流量的多拓扑链路权值优化算法

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Regression models for grouped survival data: Estimation and sensitivity analysis

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一种基于UMTS核心网的定时器实现方案

针对UMTS核心网呼叫连接协议的实现,提出了一种软件定时器的实现方案。该方案以WinCE实时嵌入式系统作为开发环境,采用单链表队列和相对时间项方式的定时器数据结构,基于时间相对算法建立了定时器单一线程和插入、删除程序,以实现协议所规定的多定时器逻辑功能。在嵌入式系统编程过程中,运用互斥量机制完成定时器线程与插入、删除程序的通信同步;运用堆管理机制完成系统内存的动态分配与释放。对该方案的主要技术和实现结构作了论述,并给出了关键代码;还对UMTS协议结构和WinCE相关机制作了分析。实验表明,该设计方案具有编程效率高、实时性能好和系统开销小的特点,适合基于嵌入式系统的通信协议定时器编程。     

SQL Server数据库的简易备份方案

提出了一种适合小型企业的,用Access数据库备份SQL数据库的方案,很好地降低了企业的成本并达到了数据备份的目的。同时,就Access数据库的安全接口作了简略讨论,使备份后不致丧失数据安全性。     

扩充Visual Basic数学计算功能的一种有效途径

阐述了在Visual Basic应用程序中如何利用动态链接库调用Matlab算法,并将其与Matlab ActiveX自动化服务器技术进行了比较,说明了一种扩充Visual Basic数学计算功能的有效途径。     

OSPF路由技术及其在IP宽带网上的应用

随着计算机网络的不断扩大,选择一种合适的路由技术尤为重要本文从ＯＳＰＦ的链路状态数据库,最短路径优先算法和ＯＳＰＦ通信协议等几个方面分析了ＯＳＰＦ的路由技术,说明了采用此技术在网络扩展性,安全性和收敛性方面具有一定优势,同时阐述了在某重大ＩＰ宽带网中采用此技术所进行的路由规划和优化工作,它对于保证ＩＰ宽带网的扩展性和健壮性,优化网络性能具有重要的意义。     

分布实时系统中的点到点通信

本文给出了一种由点到点互连网络连接的实时系统通信方法,分析了通道建立过程,消息调度算法,缓冲预留方案,以及消息传递时间的计算模型,可提供确定性的消息传递延迟。     

基于FPGA的CCD相机的图像采集

介绍在特定环境下CCD相机图像数据的采集背景下的一种基于FPGA的高速图像数据采集板卡设计方法,详细讨论了其系统结构和核心部分的内部功能模块,并给出了其核心：PCI IPcore的具体仿真结果。同时探讨了此板卡和外部的接口实现问题。