11 citations · 28 across the 6 of their papers we have counts for
6 papers
SMGRL: Scalable Multi-resolution Graph Representation Learning
Reza Namazi, Elahe Ghalebi, Sinead Williamson +1
Graph convolutional networks (GCNs) allow us to learn topologically-aware node embeddings, which can be useful for classification or link prediction. However, they are unable to ca…
On Evaluation Metrics for Graph Generative Models
Rylee Thompson, Boris Knyazev, Elahe Ghalebi +2
In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), mak…
Building LEGO Using Deep Generative Models of Graphs
Rylee Thompson, Elahe Ghalebi, Terrance DeVries +1
Generative models are now used to create a variety of high-quality digital artifacts. Yet their use in designing physical objects has received far less attention. In this paper, we…
A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu +2
As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure th…
Sequential Edge Clustering in Temporal Multigraphs
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu +2
Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner…
Dynamic Network Model from Partial Observations
Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu +1
Can evolving networks be inferred and modeled without directly observing their nodes and edges? In many applications, the edges of a dynamic network might not be observed, but one…