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Estimating Spatial Autocorrelation with Sampled Network Data
NetworkDataAnalysis Paired Maximum Likelihood Estimator
2016/1/26
Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation re...
Compressive Network Analysis
network data analysis compressive sensing Radon basis pursuit restricted isometry property clique detection
2016/1/25
Modern data acquisition routinely produces massive amounts of network data.Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected ...
Compressive Network Analysis
network data analysis compressive sensing Radon basis pursuit
2016/1/20
Modern data acquisition routinely produces massive amounts of network data.Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected ...
An Interior-Point Method for Large Scale Network Utility Maximization
An Interior-Point Method Large Scale Network Utility Maximization
2015/7/10
We describe a specialized truncated Newton primal-dual interior-point method that solves large scale network utility maximization problems, with concave utility functions, efficiently and reliably. Ou...
Graph cluster randomization: network exposure to multiple universes
Graph cluster randomization network exposure multiple universes
2013/6/17
A/B testing is a standard approach for evaluating the effect of online experiments; the goal is to estimate the `average treatment effect' of a new feature or condition by exposing a sample of the ove...
Relationships between eigen and complex network techniques for the statistical analysis of climate data
eigen complex network techniques statistical analysis climate data
2013/6/17
Eigen techniques such as empirical orthogonal function (EOF) or coupled pattern (CP) analysis have been frequently used for detecting patterns in multivariate climatological data sets. Recently, stati...
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...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
Joint likelihood calculation for intervention and observational data from a Gaussian Bayesian network
Gaussian Bayesian networks causal effects intervention data Fisher information
2013/6/13
Methodological development for the inference of gene regulatory networks from transcriptomic data is an active and important research area. Several approaches have been proposed to infer relationships...
Measuring the likelihood of models for network evolution
Measuring the likelihood models network evolution
2013/4/28
Many researchers have hypothesised models which explain the evolution of the topology of a target network. The framework described in this paper gives the likelihood that the target network arose from...
Network analysis reveals distinct clinical syndromes underlying acute mountain sickness
Acute mountain sickness sleep headache visual analogue scale BioLayout Express 3D
2013/5/2
Acute mountain sickness (AMS) is a common problem among visitors at high altitude, and may progress to life-threatening pulmonary and cerebral oedema in a minority of cases. International consensus de...
Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relativ...
A dependent partition-valued process for multitask clustering and time evolving network modelling
A dependent partition-valued process multitask clustering time evolving network modelling
2013/4/27
The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach wo...
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...
NetSimile: A Scalable Approach to Size-Independent Network Similarity
NetSimile Scalable Approach Size-Independent Network Similarity
2012/11/23
Given a set of k networks, possibly with different sizes and no overlaps in nodes or edges, how can we quickly assess similarity between them, without solving the node-correspondence problem? Analogou...