80 citations · 137 across the 12 of their papers we have counts for
12 papers
A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks
Zhipeng Huang, Hadeel Soliman, Subhadeep Paul +1
Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains. We propose the latent space Hawkes (LSH)…
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
Hadeel Soliman, Lingfei Zhao, Zhipeng Huang +2
The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption a…
Identifying Peer Influence in Therapeutic Communities Adjusting for Latent Homophily
Shanjukta Nath, Keith Warren, Subhadeep Paul
We investigate peer role model influence on successful graduation from Therapeutic Communities (TCs) for substance abuse and criminal behavior. We use data from 3 TCs that kept rec…
Testing for the Network Small-World Property
Kartik Lovekar, Srijan Sengupta, Subhadeep Paul
Researchers have long observed that the ``small-world" property, which combines the concepts of high transitivity or clustering with a low average path length, is ubiquitous for ne…
Joint Latent Space Model for Social Networks with Multivariate Attributes
Selena Shuo Wang, Subhadeep Paul, Paul De Boeck
In many application problems in social, behavioral, and economic sciences, researchers often have data on a social network among a group of individuals along with high dimensional…
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation
Makan Arastuie, Subhadeep Paul, Kevin S. Xu
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…
Higher-Order Spectral Clustering under Superimposed Stochastic Block Model
Subhadeep Paul, Olgica Milenkovic, Yuguo Chen
Higher-order motif structures and multi-vertex interactions are becoming increasingly important in studies that aim to improve our understanding of functionalities and evolution pa…
A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging
Subhadeep Paul, Yuguo Chen
Motivated by multi-subject experiments in neuroimaging studies, we develop a modeling framework for joint community detection in a group of related networks, which can be considere…
Spectral and matrix factorization methods for consistent community detection in multi-layer networks
Subhadeep Paul, Yuguo Chen
We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clu…
Null Models and Community Detection in Multi-Layer Networks
Subhadeep Paul, Yuguo Chen
Multi-layer networks are networks on a set of entities (nodes) with multiple types of relations (edges) among them where each type of relation/interaction is represented as a netwo…
Orthogonal symmetric non-negative matrix factorization under the stochastic block model
Subhadeep Paul, Yuguo Chen
We present a method based on the orthogonal symmetric non-negative matrix tri-factorization of the normalized Laplacian matrix for community detection in complex networks. While th…
Community detection in multi-relational data with restricted multi-layer stochastic blockmodel
Subhadeep Paul, Yuguo Chen
In recent years there has been an increased interest in statistical analysis of data with multiple types of relations among a set of entities. Such multi-relational data can be rep…