社团挖掘和话题监控的互动模型研究

社团挖掘是Web信息挖掘领域的重要应用,而话题监控是文本信息研究领域的重要应用,目前这两种技术是各自独立的。为更好地应用于互联网形成的复杂社会网络,将这两种技术结合起来研究,发现了社团和话题之间的关系,创建了社团挖掘和话题监控的静态和动态互动模型,设计了社团挖掘、话题识别以及社团跟踪算法。     

Detecting Overlapping Community Structures in Networks

Community structure has been recognized as an important statistical feature of networked systems over the past decade. A lot of work has been done to discover isolated communities from a network, and the focus was on developing of algorithms with high quality and good performance. However, there is less work done on the discovery of overlapping community structure, even though it could better capture the nature of network in some real-world applications. For example, people are always provided with varying characteristics and interests, and are able to join very different communities in their social network. In this context, we present a novel overlapping community structures detecting algorithm which first finds the seed sets by the spectral partition and then extends them with a special random walks technique. At every expansion step, the modularity function Q is chosen to measure the expansion structures. The function has become one of the popular standards in community detecting and is defined in Newman and Girvan (Phys. Rev. 69:026113, 2004). We also give a theoretic analysis to the whole expansion process and prove that our algorithm gets the best community structures greedily. Extensive experiments are conducted in real-world networks with various sizes. The results show that overlapping is important to find the complete community structures and our method outperforms the C-means in quality.     

基于SSH架构的社区卫生管理系统设计与实现

针对传统社区卫生管理系统开发的不足,基于J2EE企业级应用开发平台,整合Struts、Spring和Hibernate（SSH）开源框架技术,提出了一种多层社区卫生管理信息系统模型,实践表明,该整合框架在构建复杂业务系统中提高了开发效率和系统的可维护性,同时增强了系统的可扩展性。     

基于IRC日志的分享型社区结构的研究

用户为导向的在线分享型社区近几年发展迅速,而这些社区与以文字信息为基础的传统社区有较大的不同。利用IRC音乐频道从2001年到2006年的用户日志数据,构建了此共享型社区的用户网络。在对此网络的特点分析之后,将该网络的结构由有向的加权图转换为单向的二部图,从而利用层级聚类的方法,对网络中的用户群落进行了发掘。在此基础上,进一步分析了用户群落的大小,形状和分布的情况。最后根据研究的结果,提出了针对该社区提高服务的方法。     

一种改进的蚁群算法在知识路由系统中的应用

语义Web上分布着海量知识,如何在这些海量知识中按照用户的需求快速准确地定位目标知识便成为知识路由研究中亟待解决的问题。针对此问题,应用了蚁群算法作为动态虚拟语义社区中的知识路由算法,并对算法的选择策略、信息素修改两个方面进行改进,使算法不易陷入局部最优解,并能快速收敛到全局最优解。实验结果表明其路由效率明显提高,具有较好的性能。     

基于兴趣社群的P2P网络节点自治管理方法

随着P2P应用领域的扩大及用户群体的迅速扩张,对P2P网络进行节点管理、安全协同是保障其正常运行的重要条件之一。根据P2P节点的自组织及自治性与人类社会自治管理的对应关系,通过设计自组织社群构造算法,动态配置节点,给出一种基于兴趣社群的P2P网络节点自治管理方法。在JXTA平台上的实验结果表明,所提出的方法改善了P2P网络节点的可管理性,具有良好的自适应性与安全性。     

用VRML和Java实现虚拟小区的交互漫游

为了实现虚拟小区的交互漫游,在系统实现过程中采用了几种方式的交互技术,文中对这些交互技术进行了讨论和实现。阐述了在虚拟小区中交互漫游的两种方式,即由用户交互控制视点和由计算机自动控制视点的方式,并对这两种交互漫游方式给出了VRML编码的具体实现;最后采用外部编程接口（EAI）实现了用户界面中的二维平面图和三维场景图的交互。     

Set pair three-way overlapping community discovery algorithm for weighted social internet of things

There are many problems in Social Internet of Things(IoTs), such as complex topology information, different degree of association between nodes and overlapping communities. The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure. A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things (WSIoT-SPTOCD) is proposed. In the local network structure, which fully considers the topological information between nodes, the set pair connection degree is used to analyze the identity, difference and reverse of neighbor nodes. The similarity degree of different neighbor nodes is defined from network edge weight and node degree, and the similarity measurement method of set pair between nodes based on the local information structure is proposed. According to the number of nodes' neighbors and the connection degree of adjacent edges, the clustering intensity of nodes is defined, and an improved algorithm for initial value selection of k-means is proposed. The nodes are allocated according to the set pair similarity between nodes and different communities. Three-way community structures composed of a positive domain, boundary domain and negative domain are generated iteratively. Next, the overlapping node set is generated according to the calculation results of community node membership. Finally, experiments are carried out on artificial networks and real networks. The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information, overlapping community modularity and F1.     

基于社团多特征融合嵌入表示的时序链路预测方法

时序动态网络在静态网络基础上综合了时间属性的概念,包含了网络结构的复杂性、动态性等内涵,是研究复杂网络链路预测问题的较优思维对象,因在现实世界中具有较高应用价值而备受关注。目前大部分传统方法研究对象仍局限于静态网络,存在对网络时域演化信息利用不充分、时间复杂度较高等问题。结合社会学理论,提出一种基于社团多特征融合嵌入表示的时序链路预测方法,该方法的核心思想是通过分析网络动态演化特性,在社团范围内学习节点的嵌入表示向量,融合多特征以衡量节点间连边的生成概率。利用网络集体影响力的方法对节点和连边的权值进行计算,基于集体影响的连边权值进行社团划分,将网络划分为若干个社团子图,得到基于集体影响的相似性指标。在社团范围内,利用有偏的随机游走,结合梯度优化的Skip-gram方法获取所有节点的嵌入表示向量,得到基于社团范围游走的相似性指标。融合节点的集体影响、社团范围节点的多个中心性特征和学习到的节点表示向量,得到多特征融合的相似性指标,3 种新指标都可以用于衡量节点之间形成连边的概率。对比基于移动平均、嵌入表示、图神经网络等经典时序链路预测方法,在 6 个真实数据集上的实验结果表明,所提基于社团多特征融合的方法在 AUC评价标准下取得更优的预测性能。     

基于贪婪策略的紧密k核子图查询

k核查询是一种社团查询,由于其可以在线性时间内被有效计算,因此在社团检测中具有较广泛的应用。图中边的权值在很多场景下具有较强的语义关系,但现有研究较少考虑图中边的权值。为提升k核查询的效率,在k核的基础上定义加权图中的紧密k核子图查询（CRKSQ）问题,并使用归约方法证明该问题是NP-难的。基于贪婪策略设计启发式算法CRK-G,通过迭代删除节点为CRKSQ问题找到一个近似解。在此基础上,从降低图规模和减少迭代次数两方面研究CRK-G算法的优化策略,分别提出使用图压缩策略的算法CRK-C及使用单次多节点删除策略的算法CRK-F。在Bio-GRID、Email-Enron、DBLP 3个数据集上的实验结果表明,相对于CRK-G算法,CRK-C、CRK-F算法在查询速度上有较大的提升,且平均误差均在8%以内。