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Found 5 papers, 2 papers with code
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
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 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.
