Characterizing user navigation through complex data structures

As part of the general study of user navigation through interactive databases, a set of indices is proposed to characterise users' search sequences. These indices are presented as an attempt to capture some of the psychologically significant aspects of the movement of users within interactive data-bases. An example of the use of the indices is given in reference to an experiment to compare front-ends to a data-base program. Their strengths and weaknesses are discussed in the light of this experiment. Their potential as a basis for the precise definition of search strategies such as browsing and scanning is also considered.     

关于提取Web用户浏览行为特征的研究

当前,Web日志挖掘技术已成为实现网站个性化服务的研究热点.运用Markov模型来预测用户的浏览模式,从而提高站点访问率、为站点重组提供有利信息是该领域广泛采用的方法之一.但传统方法建立的Markov模型,存在着数据冗余复杂、模型庞大繁琐等问题.针对这些问题,介绍了一种改进的Markov模型.其方法主要是在原有模型的基础之上,在数据清洗、用户会话识别过程中删除一些不予考虑的因素,大大简化了建立的Markov模型,提高了Web日志挖掘的效率.     

Finding patterns in the degree distribution of real-world complex networks: going beyond power law

Pattern Analysis and Applications - The most important structural characteristics in the study of large-scale real-world complex networks in pattern analysis are degree distribution. Empirical...     

Characterizing spatial patterns of invasive species using sub-pixel classifications

Invasive species disrupt landscape patterns and compromise the functionality of ecosystem processes. Non-native saltcedar poses significant threats to native vegetation and groundwater resources in the southwestern U.S. and Mexico, and quantifying spatial and temporal distribution patterns is essential for monitoring its spread. Considerable research focuses on determining the accuracy of various remote sensing techniques for distinguishing saltcedar from native woody riparian vegetation through sub-pixel, or soft classifications. However, there is a lack of research quantifying spatial distribution patterns from these classifications, mainly because landscape metrics, which are commonly used to statistically assess these patterns, require bounded classes and cannot be applied directly to soft classifications. This study tests a new method for discretizing sub-pixel data to generate landscape metrics using a continuum of fractional cover thresholds. The developed approach transforms sub-pixel classifications into discrete maps compliant with metric terms and computes and interprets metric results in the context of the region to explain patterns in the extent, distribution, and connectivity of saltcedar in the Rio Grande basin. Results indicate that landscape metrics are sensitive to sub-pixel values and can vary greatly with fractional cover. Therefore spectral unmixing should be performed prior to metric calculations. Analysis of metric trends provides evidence that saltcedar has expanded away from the immediate riparian zones and is displacing native vegetation. This information, coupled with control management strategies, can be used to target remediation activities along the Rio Grande.     

复杂网络上的传播动力学及其新进展

深刻理解传染病在社会群体中的传播规律、计算机病毒在因特网上的扩散过程是复杂系统和传染病动力学领域研究的一个热点问题．主要从复杂网络的拓扑结构和流行病的感染机制2个方面综述了当前国内外传播动力学研究的现状和最新进展,探讨了传播动力学在疾病预防与免疫中的应用,最后指出值得进一步研究的问题．例如带社区、层次结构的加权演化模型,动态网络结构下的疾病传播行为和微观感染机制等．     

Characterizing geometric patterns formable by oblivious anonymous mobile robots

In a system in which anonymous mobile robots repeatedly execute a “Look–Compute–Move” cycle, a robot is said to be oblivious if it has no memory to store its observations in the past, and hence its move depends only on the current observation. This paper considers the pattern formation problem in such a system, and shows that oblivious robots can form any pattern that non-oblivious robots can form, except that two oblivious robots cannot form a point while two non-oblivious robots can. Therefore, memory does not help in forming a pattern, except for the case in which two robots attempt to form a point. Related results on the pattern convergence problem are also presented.     

Expansion and decentralized search in complex networks

Borrowing from concepts in expander graphs, we study the expansion properties of real-world, complex networks (e.g., social networks, unstructured peer-to-peer, or P2P networks) and the extent to which these properties can be exploited to understand and address the problem of decentralized search. We first produce samples that concisely capture the overall expansion properties of an entire network, which we collectively refer to as the expansion signature. Using these signatures, we find a correspondence between the magnitude of maximum expansion and the extent to which a network can be efficiently searched. We further find evidence that standard graph-theoretic measures, such as average path length, fail to fully explain the level of “searchability” or ease of information diffusion and dissemination in a network. Finally, we demonstrate that this high expansion can be leveraged to facilitate decentralized search in networks and show that an expansion-based search strategy outperforms typical search methods.     

Computing on anonymous networks. I. Characterizing the solvablecases

In anonymous networks, the processors do not have identity numbers. We investigate the following representative problems on anonymous networks: (a) the leader election problem, (b) the edge election problem, (c) the spanning tree construction problem, and (d) the topology recognition problem. On a given network, the above problems may or may not be solvable, depending on the amount of information about the attributes of the network made available to the processors. Some possibilities are: (1) no network attribute information at all is available, (2) an upper bound on the number of processors in the network is available, (3) the exact number of processors in the network is available, and (4) the topology of the network is available. In terms of a new graph property called “symmetricity”, in each of the four cases (1)-(4) above, we characterize the class of networks on which each of the four problems (a)(d) is solvable. We then relate the symmetricity of a network to its 1- and 2-factors