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Topological landscape is introduced for networks with functions defined on the nodes. By extending the notion of gradient flows to the net-work setting, critical nodes of different indices are defined...
Comparision of clustering methods for genetic networks
Comparision clustering methods genetic networks
2016/1/19
The goal of network clustering algorithms is to detect dense clusters in a network, which provides a first step towards the understanding of large scale biological networks. With numerous recent advan...
A Heuristic for Optimizing Stochastic Activity Networks with Applications to Statistical Digital Circuit Sizing
A Heuristic Optimizing Stochastic Activity Networks Applications Statistical Digital Circuit Sizing
2015/7/10
A deterministic activity network (DAN) is a collection of activities, each with some duration, along with a set of precedence constraints, which specify that activities begin only when certain others ...
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization
Structural Functional Discovery Dynamic Networks Non-negative Matrix Factorization
2013/6/17
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix ...
Adapting the Stochastic Block Model to Edge-Weighted Networks
Adapting Stochastic Block Model Edge-Weighted Networks
2013/6/14
We generalize the stochastic block model to the important case in which edges are annotated with weights drawn from an exponential family distribution. This generalization introduces several technical...
Optimal Periodic Sensor Scheduling in Large-Scale Dynamical Networks
Dynamic system state estimation sensor scheduling sparsity sensor networks
2013/6/14
We consider the problem of finding optimal time-periodic sensor schedules for estimating the state of a large-scale dynamical system. We assume that a large number of sensors have been deployed and th...
Estimating Network Degree Distributions Under Sampling: An Inverse Problem, with Applications to Monitoring Social Media Networks
Estimating Network Degree Distributions Sampling An Inverse Problem Applications Monitoring Social Media Networks
2013/6/14
Networks are a popular tool for representing elements in a system and their interconnectedness. Many observed networks can be viewed as only samples of some true underlying network. Such is frequently...
Meta Path-Based Collective Classification in Heterogeneous Information Networks
Heterogeneous information networks Meta path Collective classi
2013/6/17
Collective classification has been intensively studied due to its impact in many important applications, such as web mining, bioinformatics and citation analysis. Collective classification approaches ...
Modeling Temporal Activity Patterns in Dynamic Social Networks
Activity Profile Modeling Twitter Data-Fitting Explanation Prediction Hidden Markov Model Coupled Hidden Markov Model Social Network In uence User Clustering
2013/6/14
The focus of this work is on developing probabilistic models for user activity in social networks by incorporating the social network influence as perceived by the user. For this, we propose a coupled...
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
Deep belief network restricted Boltzmann machine universal approxima-tion representational power Kullback-Leibler divergence,q-ary variable
2013/4/28
We generalize recent theoretical work on the minimal number of layers of narrow deep belief networks that can approximate any probability distribution on the states of their visible units arbitrarily ...
Detecting Overlapping Temporal Community Structure in Time-Evolving Networks
Detecting Overlapping Temporal Community Structure Time-Evolving Networks
2013/5/2
We present a principled approach for detecting overlapping temporal community structure in dynamic networks. Our method is based on the following framework: find the overlapping temporal community str...
Expectation Propagation for Neural Networks with Sparsity-promoting Priors
expectation propagation neural network multilayer perceptron linear model sparse prior automatic relevance determination
2013/4/28
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation ...
We propose a general Bayesian network model for application in a wide class of problems of therapy monitoring. We discuss the use of stochastic simulation as a computational approach to inference on t...
Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks
Adaptive Non-myopic Quantizer Design Target Tracking Wireless Sensor Networks
2013/4/27
In this paper, we investigate the problem of non-myopic (multi-step ahead) quantizer design for target tracking using a wireless sensor network. Adopting the alternative conditional posterior Cramer-R...
Analysis of Partially Observed Networks via Exponential-family Random Network Models
Analysis Partially Observed Networks via Exponential-family Random Network Models
2013/4/27
Exponential-family random network (ERN) models specify a joint representation of both the dyads of a network and nodal characteristics. This class of models allow the nodal characteristics to be model...