Link Prediction for Partially Observed Networks

Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples, that is, edges known for certain to be absent, which creates a difficulty for existing supervised learning approaches. We develop a new method that treats the observed network as a sample of the true network with different sampling rates for positive (true edges) and negative (absent edges) examples. We obtain a relative ranking of potential links by their probabilities, using information on network topology as well as node covariates if available. The method relies on the intuitive assumption that if two pairs of nodes are similar, the probabilities of these pairs forming an edge are also similar. Empirically, the method performs well under many settings, including when the observed network is sparse. We apply the method to a protein–protein interaction network and a school friendship network.     

Methodological Issues in Studying Effects of Networks in Organizations

Three methodological issues are discussed that are important for the analysis of data on networks in organizations. The first is the two-level nature of the data: individuals are nested in organizations. This can be dealt with by using multilevel statistical methods. The second is the complicated nature of statistical methods for network analysis. The third issue is the potential of mathematical modeling for the study of network effects and network evolution in organizations. Two examples are given of mathematical models for gossip in organizations. The first example is a model for cross-sectional data, the second is a model for longitudinal data that reflect the joint development of network structure and individual behavior tendencies.     

Key Distribution Patterns Using Tangent Circle Structures

The problem of key management in a communications network is of primary importance. A key distribution pattern is an incidence structure which provides a secure method of distributing keys in a large network reducing storage requirements. It is of interest to find explicit constructions for key distribution patterns. In O'Keefe [5–7], examples are shown using the finite circle geometries (Minkowski, Laguerre and inversive planes); in Quinn [12], examples are constructed from conics in finite projective and affine planes. In this paper, we construct some examples using the finite tangent-circle structures, introduced in Quattrocchi and Rinaldi [10] and we give a comparison of the storage requirements.     

Inferring missing links in partially observed social networks

Determining the pattern of links within a large social network is often problematic due to the labour-intensive nature of the data collection and analysis process. With constrained data collection capabilities it is often only possible to either make detailed observations of a limited number of individuals in the network, or to make fewer observations of a larger number of people. Previously we have shown how detailed observation of a small network can be used, which infer where in the network previously unconnected individuals are likely to fit, thereby attempting to predict network growth as new people are considered for inclusion. Here, by contrast, we show how social network topology can be inferred following a limited observation of a large network. Essentially the issue is one of inferring the presence of links that are missed during a constrained data collection campaign on the network. It is particularly difficult to infer network structures for those organizations that actively seek to remain covert and undetected. However, it is often very useful to know if two given individuals are likely to be connected even though limited surveillance effort yields no evidence of a link. Specifically, we show how a statistical inference technique can be used to successfully predict the existence of links that are missed during network sampling. The procedure is demonstrated using network data obtained from open source publications.     

一个解决线性互补问题的新型神经网络

本文构造了一个新型的解决线性互补问题的神经网络,不同于那些运用罚函数和拉格朗日函数的神经网络,它的结构简单,易于计算,我们证明了该神经网络的全局收敛性和稳定性,并给出数值实验检验其有效性。     

A dynamic system model for solving convex nonlinear optimization problems

This paper proposes a feedback neural network model for solving convex nonlinear programming (CNLP) problems. Under the condition that the objective function is convex and all constraint functions are strictly convex or that the objective function is strictly convex and the constraint function is convex, the proposed neural network is proved to be stable in the sense of Lyapunov and globally convergent to an exact optimal solution of the original problem. The validity and transient behavior of the neural network are demonstrated by using some examples.     

Neural networks for a class of bi-level variational inequalities

In this paper, a class of bi-level variational inequalities for describing some practical equilibrium problems, which especially arise from engineering, management and economics, is presented, and a neural network approach for solving the bi-level variational inequalities is proposed. The energy function and neural dynamics of the proposed neural network are defined in this paper, and then the existence of the solution and the asymptotic stability of the neural network are shown. The simulation algorithm is presented and the performance of the proposed neural network approach is demonstrated by some numerical examples.     

A Bayesian network model for spatial event surveillance

Methods for spatial cluster detection attempt to locate spatial subregions of some larger region where the count of some occurrences is higher than expected. Event surveillance consists of monitoring a region in order to detect emerging patterns that are indicative of some event of interest. In spatial event surveillance, we search for emerging patterns in spatial subregions.A well-known method for spatial cluster detection is Kulldorff’s [M. Kulldorff, A spatial scan statistic, Communications in Statistics: Theory and Methods 26 (6) (1997)] spatial scan statistic, which directly analyzes the counts of occurrences in the subregions. Neill et al. [D.B. Neill, A.W. Moore, G.F. Cooper, A Bayesian spatial scan statistic, Advances in Neural Information Processing Systems (NIPS) 18 (2005)] developed a Bayesian spatial scan statistic called BSS, which also directly analyzes the counts.We developed a new Bayesian-network-based spatial scan statistic, called BNetScan, which models the relationships among the events of interest and the observable events using a Bayesian network. BNetScan is an entity-based Bayesian network that models the underlying state and observable variables for each individual in a population.We compared the performance of BNetScan to Kulldorff’s spatial scan statistic and BSS using simulated outbreaks of influenza and cryptosporidiosis injected into real Emergency Department data from Allegheny County, Pennsylvania. It is an open question whether we can obtain acceptable results using a Bayesian network if the probability distributions in the network do not closely reflect reality, and thus, we examined the robustness of BNetScan relative to the probability distributions used to generate the data in the experiments. Our results indicate that BNetScan outperforms the other methods and its performance is robust relative to the probability distribution that is used to generate the data.     

Dynamic network reliability modeling under nonhomogeneous Poisson processes

In this paper, we consider a two-state (up and down) network consisting of n links. We study the D-spectrum based dynamic reliability of the network under the assumption that the links are subject to failure according to a nonhomogeneous Poisson process. Several mixture representations are provided for the reliability function of residual lifetime of used networks, under different conditions on the status of the network or its links. These representations enable us to explore the residual reliability of operating networks in terms of the reliability functions of residual lifetimes of upper record values. The distribution function of inactivity time of a network is examined under the condition that the network has failed by inspection time t. Stochastic ordering properties of the residual lifetimes of networks under conditional D-spectra are investigated. Several examples and graphs are also provided to illustrate the established results.     

Network science faces the challenge and opportunity:exploring ``Network of Networks" and its unified theoretical framework

In the era of big data, network science is facing new challenges and opportunities. This review article focuses on discussing one of the hottest subjects of network science - ``network of networks" (NON). The main features, several typical examples and the main progress for NON are outlined, including the epidemic spreading in multilayer coupled networks. Finally the most challenging tasks for NON are proposed.     

A BRIEF INTRODUCTION TO SCALE‐FREE NETWORKS

ABSTRACT. This article provides a brief introduction to scale‐free networks. The notion of a scale‐free network is defined and some examples given. Properties frequently exhibited by scale‐free networks are discussed. The importance of the phenomenon of preferential attachment in generating scale‐free networks is illustrated with two examples for the spread of a persistent disease. The models are similar in that they both yield a total infected population (1) which is geometrically distributed, and growing exponentially in expectation; and (2) in which the average distance from the original source of infection grows in a similar way over time. However one model, which has preferential attachment (infection), yields a scale‐free network, while the other which has homogeneous infectivity does not. The possible application of the theory of scale‐free networks to resource management is briefly discussed.     

Exponential synchronization of the complex dynamical networks with a coupling delay and impulsive effects

Based on the stability analysis of the impulsive functional differential equation, the exponential synchronization of the complex dynamical network with a coupling delay and impulses is investigated in the paper. The criteria for the exponential synchronization are derived by the geometrical decomposition of network states and linear matrix inequality method. Two examples are given to show the effectiveness of the proposed criteria.     

关于耦合网络间同步控制的一个注记

本文旨在研究具有相互作用的两个网络间的同步及其控制问题,从理论上我们证明了通过选择适当的控制项,两个网络可以达到同步．文中没有限制两个网络具有相同的动力学,针对两个网络的拓扑结构和网络问的作用矩阵,我们提出了两种自适应同步格式,最后通过数值算例来阐述我们得到的理论结果．     

城市排水管网系统布局的优化设计

本文证明了城市排水管网系统最优布局为树状结构的定理；同时提出获得优化树的修正逐步生成法。实例计算表明，其结果令人满意。     

The effect of delayed feedback information on network performance

The performance of a network subject to either state dependent or state independent flow control is investigated. In the state dependent case, the flow control policy is a function of the total number of packets for which the controller has not yet received an acknowledgment. In this case it is shown that the optimal flow control is a sliding window mechanism. The effect of the delayed feedback on the network performance as well as the size of the window are studied. The state independent optimal rate is also derived. The performance of the state dependent and state independent flow control policies are compared. Conditions for employing one of the two types of flow control policies for superior end-to-end network performance are discussed. All the results obtained are demonstrated using simple examples.     

Asymptotical Stability for a Class of Complex-Valued Projective Neural Network

In this paper, a new class of complex-valued projective neural network is introduced and studied on a nonempty, closed, and convex subset of a finite-dimensional complex space. An existence and uniqueness result for the equilibrium point of complex-valued projective neural network is proved under some suitable conditions. Moreover, by utilizing the linear matrix inequality technique, some sufficient conditions are presented to ensure the asymptotical stability of the complex-valued projective neural network. Finally, two examples are given to illustrate the validity and feasibility of main results.     

A differential inclusion-based approach for solving nonsmooth convex optimization problems

This article presents a differential inclusion-based neural network for solving nonsmooth convex programming problems with inequality constraints. The proposed neural network, which is modelled with a differential inclusion, is a generalization of the steepest descent neural network. It is proved that the set of the equilibrium points of the proposed differential inclusion is equal to that of the optimal solutions of the considered optimization problem. Moreover, it is shown that the trajectory of the solution converges to an element of the optimal solution set and the convergence point is a globally asymptotically stable point of the proposed differential inclusion. After establishing the theoretical results, an algorithm is also designed for solving such problems. Typical examples are given which confirm the effectiveness of the theoretical results and the performance of the proposed neural network.     

A hybrid neural network approach to bilevel programming problems

A combined neural network and tabu search hybrid algorithm is proposed for solving the bilevel programming problem. To illustrate the approach, two numerical examples are solved and the results are compared with those in the literature.     

A note on the solution to a common thermal network problem encountered in heat-transfer analysis of spacecraft

A widely used discretization method for modeling thermal systems is the thermal network approach. The network approach is derived from energy balance equations and is equivalent to a particular finite difference discretization of the underlying heat-transfer equation. The steady-state problems that arise in the analysis of spacecraft systems using network models are frequently dominated by radiative transfer, which introduces quartic nonlinearities in the equations. Although these systems are routinely encountered, there has not appeared any detailed analysis of these equations. Questions of existence and uniqueness of solutions and numerical methods for solving the systems have never been addressed in any generality. In this paper, general existence and uniqueness properties of the network equations are established. Globally convergent methods for solving the systems are developed and insight into the relative success of existing methods is given. Numerical examples are presented illustrating the methods. The perspective adopted here is also useful in interdisciplinary applications. A simple example involving thermal control is used to demonstrate this.     

An MPEC formulation and its cutting constraint algorithm for continuous network design problem with multi-user classes

Continuous network design problem (CNDP) is to determine the set of link capacity expansions and the corresponding equilibrium flows for which the measures of performance index for the network is optimal. Conventionally, CNDP assumed users to be homogeneous, that is, all travelers on the same link of the network are identical insofar as congestion effect and they have the same value of time (VOT). In fact, it does not accord with the real situation that all have the same VOT. So, multiple user classes with different VOT should be considered. This paper examines the CNDP with different VOT for multiple user classes, which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). Then, the cut constraint algorithm (CCA) is presented to solve the problem. The numerical experiments on the examples from the literature are illustrated to demonstrate that our model and algorithm are feasible.