Conceptualizing means-end chains of user goals as networks

Goals are desired states that an individual tries to attain. The process of achieving a goal can be represented as interlinked means-end chains of user goals that have been traditionally visualized as hierarchies. Evidence in recent literature suggests that a network structure would be more appropriate and provide insight into a user's process of seeking a goal. We investigated user goal means-end chains for the eBay online auction system, and produced its structure as a goal network. To analyze this network and assess the importance of various goals, social network analysis measures were used (specifically, degree and flow-betweenness centrality). In addition, goal networks for users with low and high IS value were created and differences in goal importance in the two groups were considered. Results revealed that the most important user goals are closely related to key features of the auction system; users with high IS value want to use eBay to buy, sell, and bid for products, while users with low IS value seem to avoid using eBay because of uncertain price bidding. As such, the results of our study suggest that differences in IS value may be due to differences in IS usage. IS designers, marketers, and providers of online auction system can use our findings to design and promote better systems for their users.     

基于中介中心性提高复杂网络容量的方法

对于像互联网这样具有无标度特征的网络,节点的重要程度差别很大,少数中枢节点成为制约网络容量的瓶颈.引入中介中心性对网络拓扑进行优化和拥塞预测,通过理论分析和仿真实验,考察了网络中节点的介数的和、标准差,两点间最短路径长度,最短路径通过的中枢节点的个数等参数与网络容量的关系.最终提出在具有无标度特征的复杂网络中,依据网络中节点的介数以及介数的标准差增加一些捷径路径的方法.该方法简单易行,能有效平衡中枢节点的负载,缓解拥塞状况,提高网络容量.     

基于特征向量中心度量的可疑洗钱识别算法

该文介绍了当前反洗钱领域中的一些经验,并且提出了一种基于特征向量中心度量的可疑洗钱识别算法,该算法针对可疑洗钱行为中的存在的交易网络进行全局识别。同时,该文提供了一个基于MATLAB的算法仿真。     

A new centrality measure based on sub-tree

How to evaluate the importance of nodes in networks and detect the centrality has become a vital problem in improving the efficiency of telecommunication and making a disease immunity strategy. We consider the mechanisms of real networks, and define a cost function to describe different hierarchies of networks to measure node importance. This method takes up a node’s regional influence as well as its global influence to evaluate its importance. The results of simulation prove that this method is proper to describe effectively and detect node discrepancies in a network.     

基于频度中心理论的三维模型简化方法

针对三维模型传输技术中的模型简化问题,以边收缩算法思想为基础,应用网络节点频度中心理论(Degree Centrality),提出并实现了一种保持几何封闭性的模型简化方法.该方法能在保证简化模型逼真度的同时较好地保持模型的几何封闭性,避免孤立面的产生,并且生成了渐进网格文件,为模型提供了连续多分辨率的模型重建.实验证明了该方法的有效性.     

基于社会性的DTN网络路由算法研究

针对DTN网络中的消息传输问题,结合概率路由算法PROPHET和社会性路由算法SimBet的基本思想,提出一种DTN网络路由算法——ProSimbet。在选择转发节点时,综合考虑基于历史相遇记录的概率估计和节点的社会性这2个指标,从而降低消息转发次数。实验结果表明,在密集数据集INFOCOM06上,ProSimBet、SimBet、PROPHET和ER这4种路由算法的消息成功转发数目比较接近。在稀疏数据集MIT上,ProSimBet在消息转发次数上比SimBet降低26.1%,在消息成功转发数目上比PROPHET提高7.83%。     

A site entropy rate and degree centrality based algorithm for image co-segmentation

In this paper, we propose a graph based algorithm that efficiently segments common objects from multiple images. We first generate a number of object proposals from each image. Then, an undirected graph is constructed based on proposal similarities and co-saliency maps. Two different methods are followed to extract the proposals containing common objects. They are: (1) degree centrality of nodes obtained after graph thresholding and (2) site entropy rate of nodes calculated on the stationary distribution of Markov chain constructed on the graph. Finally, we obtain the co-segmented image region by selecting the more salient of the object proposals obtained by the two methods, for each image. Multiple instances of the common object are also segmented efficiently. The proposed method has been compared with many existing co-segmentation methods on three standard co-segmentation datasets. Experimental results show its effectiveness in co-segmentation, with larger IoU values as compared to other co-segmentation methods.     

Finding influential agent groups in complex multiagent software systems based on citation network analyses

Current complex engineering software systems are often composed of many components and can be built based on a multiagent approach, resulting in what are called complex multiagent software systems. In a complex multiagent software system, various software agents may cite the operation results of others, and the citation relationships among agents form a citation network; therefore, the importance of a software agent in a system can be described by the citations from other software agents. Moreover, the software agents in a system are often divided into various groups, and each group contains the agents undergoing similar tasks or having related functions; thus, it is necessary to find the influential agent group (not only the influential individual agent) that can influence the system outcome utilities more than the others. To solve such a problem, this paper presents a new model for finding influential agent groups based on group centrality analyses in citation networks. In the presented model, a concept of extended group centrality is presented to evaluate the impact of an agent group, which is collectively determined by both direct and indirect citations from other agents outside the group. Moreover, the presented model addresses two typical types of agent groups: one is the adjacent group where agents of a group are adjacent in the citation network, and the other is the scattering group where agents of a group are distributed separately in the citation network. Finally, we present case studies and simulation experiments to prove the effectiveness of the presented model.