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Found 10 papers, 4 papers with code
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.
Identifying Peer Influence in Therapeutic Communities
We investigate if there is a peer influence or role model effect on successful graduation from Therapeutic Communities (TCs).
Joint Analysis of Social and Item Response Networks with Latent Space Models
Using LSMH, we summarize the information from the social network and the item responses in a person-item joint latent space.
Applications Methodology
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.
Higher-Order Spectral Clustering under Superimposed Stochastic Block Model
In particular, we prove non-asymptotic upper bounds on the misclustering error of spectral community detection for a SupSBM setting in which triangles or 3-uniform hyperedges are superimposed with undirected edges.
Spectral and matrix factorization methods for consistent community detection in multi-layer networks
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 clustering or a low-rank matrix factorization.
Null Models and Community Detection in Multi-Layer Networks
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 network layer.
Community Detection
Methodology
Social and Information Networks
Physics and Society
Orthogonal symmetric non-negative matrix factorization under the stochastic block model
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.
Community detection in multi-relational data with restricted multi-layer stochastic blockmodel
We derive consistency results for community assignments of the maximum likelihood estimators (MLEs) in both models where MLSBM is assumed to be the true model, and either the number of nodes or the number of types of edges or both grow.
