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Found 9 papers, 4 papers with code
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and node classification.
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model
On four real social signed networks of polarization, we demonstrate that the model extracts low-dimensional characterizations that well predict friendships and animosity while providing interpretable visualizations defined by extreme positions when endowing the model with an embedding space restricted to polytopes.
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations
We further impose a scalable Kronecker structured Gaussian Process prior to the dynamics accounting for community structure, temporal smoothness, and disentangled (uncorrelated) latent embedding dimensions optimally learned to characterize the network dynamics.
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations
This paper proposes a novel scalable graph representation learning method named the Hierarchical Block Distance Model (HBDM).
Topic-aware latent models for representation learning on networks
Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node classification, link prediction, and clustering.
NodeSig: Binary Node Embeddings via Random Walk Diffusion
Graph Representation Learning (GRL) has become a key paradigm in network analysis, with a plethora of interdisciplinary applications.
Exponential Family Graph Embeddings
We introduce the generic \textit{exponential family graph embedding} model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions.
Kernel Node Embeddings
Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classification.
TNE: A Latent Model for Representation Learning on Networks
Although various approaches have been proposed to compute node embeddings, many successful methods benefit from random walks in order to transform a given network into a collection of sequences of nodes and then they target to learn the representation of nodes by predicting the context of each vertex within the sequence.
