Search Results for author:
Found 5 papers, 3 papers with code
A Simple and Powerful Framework for Stable Dynamic Network Embedding
We propose that a wide class of established static network embedding methods can be used to produce interpretable and powerful dynamic network embeddings when they are applied to the dilated unfolded adjacency matrix.
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data.
Spectral embedding and the latent geometry of multipartite networks
Spectral embedding finds vector representations of the nodes of a network, based on the eigenvectors of its adjacency or Laplacian matrix, and has found applications throughout the sciences.
Spectral embedding for dynamic networks with stability guarantees
We consider the problem of embedding a dynamic network, to obtain time-evolving vector representations of each node, which can then be used to describe changes in behaviour of individual nodes, communities, or the entire graph.
Spectral embedding of weighted graphs
When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results.
