2 citations · 2 across the 5 of their papers we have counts for
3 papers · 1 filter
Deep Generative Models for Subgraph Prediction
Erfaneh Mahmoudzadeh, Parmis Naddaf, Kiarash Zahirnia +1
Graph Neural Networks (GNNs) are important across different domains, such as social network analysis and recommendation systems, due to their ability to model complex relational da…
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf +1
Generative models for graph data are an important research topic in machine learning. Graph data comprise two levels that are typically analyzed separately: node-level properties s…
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning
Kiarash Zahirnia, Ankita Sakhuja, Oliver Schulte +3
Recent work on graph generative models has made remarkable progress towards generating increasingly realistic graphs, as measured by global graph features such as degree distributi…