Geometrically Corrected Second Order Analysis of Events on a Linear Network,with Applications to Ecology and Criminology

Abstract. We study point patterns of events that occur on a network of lines, such as road accidents recorded on a road network. Okabe and Yamada developed a ‘network K function’, analogous to Ripley's K function, for analysis of such data. However, values of the network K‐function depend on the network geometry, making interpretation difficult. In this study we propose a correction of the network K‐function that intrinsically compensates for the network geometry. This geometrical correction restores many natural and desirable properties of K, including its direct relationship to the pair correlation function. For a completely random point pattern, on any network, the corrected network K‐function is the identity. The corrected estimator is intrinsically corrected for edge effects and has approximately constant variance. We obtain exact and asymptotic expressions for the bias and variance of under complete randomness. We extend these results to an ‘inhomogeneous’ network K‐function which compensates for a spatially varying intensity of points. We demonstrate applications to ecology (webs of the urban wall spider Oecobius navus) and criminology (street crime in Chicago).     

Scalable module detection for attributed networks with applications to breast cancer

The objective of network module detection is to identify groups of nodes within a network structure that are tightly connected. Nodes in a network often have attributes (aka metadata) associated with them. It is often desirable to identify groups of nodes that are tightly connected in the network structure, but also have strong similarity in their attributes. Utilizing attribute information in module detection is a major challenge because it requires bridging the structural network with attribute data. A Weighted Fast Greedy (WFG) algorithm for attribute-based module detection is proposed. WFG utilizes logistic regression to bridge the structural and attribute spaces. The logistic function naturally emphasizes associations between attributes and network structure accordingly, and can be easily interpreted. A breast cancer application is presented that connects a protein–protein interaction network gene expression data and a survival outcome. This application demonstrates the importance of embedding attribute information into the community detection framework on a breast cancer dataset. Five modules were significant for survival and they contained known pathways and markers for cancer, including cell cycle, p53 pathway, BRCA1, BRCA2, and AURKB, among others. Whereas, neither the gene expression data nor the network structure alone gave rise to these cancer biomarkers and signatures.     

Estimating time-varying directed neural networks

Reconstructing the functional network of a neuron cluster is a fundamental step to reveal the complex interactions among neural systems of the brain. Current approaches to reconstruct a network of neurons or neural systems focus on establishing a static network by assuming the neural network structure does not change over time. To the best of our knowledge, this is the first attempt to build a time-varying directed network of neurons by using an ordinary differential equation model, which allows us to describe the underlying dynamical mechanism of network connections. The proposed method is demonstrated by estimating a network of wide dynamic range neurons located in the dorsal horn of the rats’ spinal cord in response to pain stimuli applied to the Zusanli acupoint on the right leg. The finite sample performance of the proposed method is also investigated with a simulation study.

     

城市群经济网络与经济增长——基于大数据与网络分析方法的研究

借助大数据时代下获得的海量数据,本文分析了长三角城市群的经济网络特征,重点研究了城市群经济网络的增长效应。首先,构建了长三角城市群的人口流动网络、企业组织网络与电子商务网络,对其各自的网络结构特征进行了对比。其次,将网络分析方法与空间计量模型结合起来,使用扩展的J检验方法对不同网络结构下的模型设定方法进行了识别,考察了经济网络带来的溢出效应对于城市群经济增长的影响。分析结果显示,三种经济网络下长三角城市群均呈现出了“中心-外围”的网络结构,其中上海、杭州、苏州、南京及无锡位于城市群经济网络的核心圈层。对网络结构的模型识别结果显示,中心城市在长三角城市群经济网络的溢出效应中扮演着重要角色。具体而言,在人口流动网络下,资本、政府行为存在显著为负的网络溢出效应;在企业组织网络下,人口规模、对外开放呈现出显著为正的网络溢出效应;在电子商务网络下,政府行为存在显著为负的网络溢出效应,对外开放呈现出显著为正的网络溢出效应。     

新浪微博社会网络的自组织行为研究

互联网社会网络主要用于信息传播与共享,这种行为的自组织性主要体现在信息传播。根据自组织理论,新浪微博社会网络是一个自组织系统,从整体网络、个体网络、小团体、小世界效应构建模型,并以"可持续发展"话题为例,采用"滚雪球抽样方法",考虑网络用户间的"发布、转发、评论、@、回复"关系,进行实证研究。结果表明:社会网络存在自组织行为;整体自组织现象弱,局部明显;由于网络用户角色不同,围绕网络用户形成的自组织网络凝聚强度不同。     

Phase I monitoring of social network with baseline periods using poisson regression

Social network analysis is an important analytic tool to forecast social trends by modeling and monitoring the interactions between network members. This paper proposes an extension of a statistical process control method to monitor social networks by determining the baseline periods when the reference network set is collected. We consider probability density profile (PDP) to identify baseline periods using Poisson regression to model the communications between members. Also, Hotelling T² and likelihood ratio test (LRT) statistics are developed to monitor the network in Phase I. The results based on signal probability indicate a satisfactory performance for the proposed method.     

关系网络对家族企业社会责任的影响——来自浙江和重庆两地制造业家族企业的实证

利用浙江和重庆两地制造业家族企业的调查数据,基于因子分析、多元回归分析等研究方法,实证检验了组织间关系网络对家族企业社会责任的影响以及家族所有权的调节作用,研究结果表明:(1)网络中心度对家族企业内部人责任、外部人责任和公共责任有显著的正向影响,网络密度对家族企业外部人责任有显著的正向影响,一级网络关系强度对家族企业内部人责任、外部人责任和公共责任有显著的正向影响,二级网络开放度对家族企业内部人责任和外部人责任有显著的负向影响;(2)家族所有权负向调节网络中心度与家族企业内部人责任、外部人责任和公共责任的关系。     

Distributed detection/localization of change-points in high-dimensional network traffic data

We propose a novel approach for distributed statistical detection of change-points in high-volume network traffic. We consider more specifically the task of detecting and identifying the targets of Distributed Denial of Service (DDoS) attacks. The proposed algorithm, called DTopRank, performs distributed network anomaly detection by aggregating the partial information gathered in a set of network monitors. In order to address massive data while limiting the communication overhead within the network, the approach combines record filtering at the monitor level and a nonparametric rank test for doubly censored time series at the central decision site. The performance of the DTopRank algorithm is illustrated both on synthetic data as well as from a traffic trace provided by a major Internet service provider.     

基于深度学习LSTM神经网络的全球股票指数预测研究

作为深度学习技术的经典模型之一，长短期记忆（LSTM）神经网络在挖掘序列数据长期依赖关系中极具优势。基于深度神经网络优化技术，本文构造了一个深层LSTM神经网络并将其应用于全球30个股票指数三种不同期限的预测研究，结果发现：①LSTM神经网络具有很强的泛化能力，对全部指数不同期限的预测效果均很稳定；②LSTM神经网络具有优秀的预测精度，相比三种对照模型（SVR，MLP和ARIMA），其对全部指数的平均预测精度在不同期限上均有提升；③LSTM神经网络能够有效控制误差波动，其对全部指数的平均预测稳定度相比三种对照模型在不同期限上亦均有提高。鉴于LSTM神经网络在预测精度和稳定度两方面的优势，其未来在金融预测中将有广阔的应用前景。     

The Relative Fit measure for evaluating a blockmodel

Statistical Methods & Applications - A blockmodel is a network in which the nodes are clusters of equivalent (in terms of the structure of the links connecting) nodes in the network being...     

PCA likelihood ratio test approach for attributed social networks monitoring

Abstract

One of the most important factors in building and changing communication mechanisms in social networks is considering features of the members of social networks. Most of the existing methods in network monitoring don’t consider effects of features in network formation mechanisms and others don’t lead to reliable results when the features abound or when there are correlations among them. In this article, we combined two methods principal component analysis (PCA) and likelihood method to monitor the underlying network model when the features of individuals abound and when some of them have high correlations with each other.     

Estimation of relative risk for events on a linear network

Motivated by the study of traffic accidents on a road network, we discuss the estimation of the relative risk, the ratio of rates of occurrence of different types of events occurring on a network of lines. Methods developed for two-dimensional spatial point patterns can be adapted to a linear network, but their requirements and performance are very different on a network. Computation is slow and we introduce new techniques to accelerate it. Intensities (occurrence rates) are estimated by kernel smoothing using the heat kernel on the network. The main methodological problem is bandwidth selection. Binary regression methods, such as likelihood cross-validation and least squares cross-validation, perform tolerably well in our simulation experiments, but the Kelsall–Diggle density-ratio cross-validation method does not. We find a theoretical explanation, and propose a modification of the Kelsall–Diggle method which has better performance. The methods are applied to traffic accidents in a regional city, and to protrusions on the dendritic tree of a neuron.

     

基于社会网络分析的意见领袖网结构

微博中的意见领袖主导了大量的信息流动,通过分析他们之间的关系,可以理解微博平台的传播结构。用两阶聚类分析法对新浪微博中的数据进行分析,识别出具有社交网络特征的意见领袖群体,再用社会网络分析方法将识别出来的群体构建成意见领袖网,分析该网络在中心度、密度等方面的特征。实证分析表明:网络中的成员连接关系紧密度不高,网络中没有直接关系的成员要建立联系的平均距离较近,网络中位于核心位置的成员中心化程度不高。     

A deletion/substitution/addition algorithm for classification neural networks,with applications to biomedical data

Neural networks are a popular machine learning tool, particularly in applications such as protein structure prediction; however, overfitting can pose an obstacle to their effective use. Due to the large number of parameters in a typical neural network, one may obtain a network fit that perfectly predicts the learning data, yet fails to generalize to other data sets. One way of reducing the size of the parmeter space is to alter the network topology so that some edges are removed; however it is often not immediately apparent which edges should be eliminated. We propose a data-adaptive method of selecting an optimal network architecture using a deletion/substitution/addition algorithm. Results of this approach to classification are presented on simulated data and the breast cancer data of Wolberg and Mangasarian [1990. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Nat. Acad. Sci. 87, 9193–9196].     

邮轮旅游网络关注度的时空特征和影响因素——基于百度指数的研究

百度指数作为一个数据获取的重要工具和途径,对分析和研究相关问题具有重要作用。基于百度指数系统收集了中国各地区邮轮旅游网络关注度的数据,进行了时空特征和影响因素的研究。研究发现:邮轮旅游网络关注度具有时间差异较大,变化显著等特征;邮轮旅游网络关注度根据空间特征可以划分为四种类型:关注强势区、关注发展区、关注平稳区、关注弱势区;各区域邮轮旅游网络关注度指数主要受到人均GDP和网民数量的影响。     

Adaptive sampling without replacement of clusters

In a common form of adaptive cluster sampling, an initial sample of units is selected by random sampling without replacement and, whenever the observed value of the unit is sufficiently high, its neighboring units are added to the sample, with the process of adding neighbors repeated if any of the added units are also high valued. In this way, an initial selection of a high-valued unit results in the addition of the entire network of surrounding high-valued units and some low-valued “edge” units where sampling stops. Repeat selections can occur when more than one initially selected unit is in the same network or when an edge unit is shared by more than one added network. Adaptive sampling without replacement of networks avoids some of this repeat selection by sequentially selecting initial sample units only from the part of the population not already in any selected network. The design proposed in this paper carries this step further by selecting initial units only from the population, exclusive of any previously selected networks or edge units.     

复杂产业网络度中心性研究

近年来,基于图论的网络科学方法在经济分析领域受到较多关注,但如何正确使用有着不同的观点。本文将投入产出经济学与网络科学相结合,视经济系统为一个循环流,形成连接稠密、有向加权及自循环的复杂网络拓扑结构,以需求驱动的后向关联技术网络和供给驱动的前向关联技术网络分别构建入强度、出强度、总强度及综合强度中心性指标,并给出相应的经济解释。基于我国2017年投入产出表的测算结果表明,有向加权网络度中心性有效克服了由威弗组合指数形成的有向无权网络度中心性与关联系数矩阵的变异系数相关性显著的缺点,其测算结果更符合产业部门的经济基础特征,且具有信息熵更大、分辨率更高的特点。研究表明,基于有向加权产业网络可以构造出更加全面和有效的度中心性指标,对关键产业部门的识别能够起到补充和完善作用。     

Improving Path Trimming in a Network Algorithm for Fisher's Exact Test in Two-way Contingency Tables

This article provides an improvement of the network algorithm for calculating the exact p value of the generalized Fisher's exact test in two-way contingency tables. We give a new exact upper bound and an approximate upper bound for the maximization problems encountered in the network algorithm. The approximate bound has some very desirable computational properties and the meaning is elucidated from a viewpoint of differential geometry. Our proposed procedure performs well regardless of the pattern of marginal totals of data.     

A Statistical Model for Social Network Labeling

We consider a social network from which one observes not only network structure (i.e., nodes and edges) but also a set of labels (or tags, keywords) for each node (or user). These labels are self-created and closely related to the user’s career status, life style, personal interests, and many others. Thus, they are of great interest for online marketing. To model their joint behavior with network structure, a complete data model is developed. The model is based on the classical p₁ model but allows the reciprocation parameter to be label-dependent. By focusing on connected pairs only, the complete data model can be generalized into a conditional model. Compared with the complete data model, the conditional model specifies only the conditional likelihood for the connected pairs. As a result, it suffers less risk from model misspecification. Furthermore, because the conditional model involves connected pairs only, the computational cost is much lower. The resulting estimator is consistent and asymptotically normal. Depending on the network sparsity level, the convergence rate could be different. To demonstrate its finite sample performance, numerical studies (based on both simulated and real datasets) are presented.