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Found 17 papers, 4 papers with code
A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation
We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative.
A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks
Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains.
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
The stochastic block model (SBM) is one of the most widely used generative models for network data.
Learning User Embeddings from Temporal Social Media Data: A Survey
User-generated data on social media contain rich information about who we are, what we like and how we make decisions.
Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs
Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease.
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation
In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data consist of timestamped relational events, which form a continuous-time network.
The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks
The BPPM is inspired by the well-known stochastic block model (SBM) for static networks.
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data
In this paper, we demonstrate that unsupervised learning algorithms perform competitively with supervised algorithms for detecting MAs on EDA data collected in both a lab-based setting and a real-world setting comprising about 23 hours of data.
Block CUR: Decomposing Matrices using Groups of Columns
In this paper, we consider matrix approximation by sampling predefined \emph{blocks} of columns (or rows) from the matrix.
Evaluating Link Prediction Accuracy on Dynamic Networks with Added and Removed Edges
Recent work by Yang et al. has highlighted several challenges in evaluating link prediction accuracy.
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis.
Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysis
If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination.
Stochastic Block Transition Models for Dynamic Networks
There has been great interest in recent years on statistical models for dynamic networks.
Consistent estimation of dynamic and multi-layer block models
Significant progress has been made recently on theoretical analysis of estimators for the stochastic block model (SBM).
Methodology Social and Information Networks Statistics Theory Physics and Society Statistics Theory
Dynamic stochastic blockmodels for time-evolving social networks
There has been recent interest in statistical modeling of dynamic networks, which are observed at multiple points in time and offer a richer representation of many complex phenomena.
Revealing social networks of spammers through spectral clustering
To date, most studies on spam have focused only on the spamming phase of the spam cycle and have ignored the harvesting phase, which consists of the mass acquisition of email addresses.
Dynamic stochastic blockmodels: Statistical models for time-evolving networks
Significant efforts have gone into the development of statistical models for analyzing data in the form of networks, such as social networks.
