Search Results for author:
Found 14 papers, 8 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.
Hierarchical clustering with dot products recovers hidden tree structure
In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure.
Implications of sparsity and high triangle density for graph representation learning
Recent work has shown that sparse graphs containing many triangles cannot be reproduced using a finite-dimensional representation of the nodes, in which link probabilities are inner products.
Graph Representation Learning
Vocal Bursts Intensity Prediction
Statistical exploration of the Manifold Hypothesis
The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space.
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.
Matrix factorisation and the interpretation of geodesic distance
Given a graph or similarity matrix, we consider the problem of recovering a notion of true distance between the nodes, and so their true positions.
Spectral clustering under degree heterogeneity: a case for the random walk Laplacian
This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree.
Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel
The proposed method is based on a transformation of the spectral embedding to spherical coordinates, and a novel modelling assumption in the transformed space.
The multilayer random dot product graph
We present a comprehensive extension of the latent position network model known as the random dot product graph to accommodate multiple graphs -- both undirected and directed -- which share a common subset of nodes, and propose a method for jointly embedding the associated adjacency matrices, or submatrices thereof, into a suitable latent space.
Manifold structure in graph embeddings
Statistical analysis of a graph often starts with embedding, the process of representing its nodes as points in space.
Spectral embedding of weighted graphs
When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results.
A statistical interpretation of spectral embedding: the generalised random dot product graph
Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph.
