Towards Scalable and Interpretable Graph Neural Networks

Collaborative Research: Towards Scalable and Interpretable Graph ...

As new generalizations of traditional deep neural networks to graph structured data, Graph Neural Networks (or GNNs) have demonstrated the power in graph ...

This project proposes novel principles and mechanisms for scalable and interpretable graph neural networks to facilitate the adoption of GNNs on ...

Towards Scalable and Interpretable Graph Neural Networks

This project proposes novel principles and mechanisms for scalable and interpretable graph neural networks to facilitate the adoption of GNNs on critical ...

Towards Interpretable Graph Neural Networks - ACM Digital Library

This 2-minute video explores our research on the Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural ...

Towards Interpretable Graph Neural Networks - arXiv

To bridge this gap, this paper proposes a novel intrinsically interpretable scheme for graph classification, termed as Global Interactive Pattern (GIP) learning ...

Toward Scalable and Efficient Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) stand out as a promising neural model class designed for heterogeneous graphs. Built on traditional ...

Scalability and interpretability of graph neural networks for small ...

I propose a novel kernel-inspired graph neural network architecture, called a subgraph matching neural network (SMNN), which is designed to have all feature ...

Interpretable and Convergent Graph Neural Network Layers at Scale

To tackle this limitation, we propose a sampling-based energy function and scalable GNN layers that iteratively reduce it, guided by convergence ...

Towards interpretable graph neural networks for transport prediction ...

In this paper, we use Neural Relational Inference to learn the optimal graph for the model. Our approach has several advantages.

Towards Scalable Adaptive Learning with Graph Neural Networks ...

Our model is a sequential recommender system based on a graph neural network, which we evaluate on a population of simulated learners. Our ...

Awesome-DynamicGraphLearning/README.md at main - GitHub

Graph Neural Networks for temporal graphs: State of the art, open challenges ... Towards Better Dynamic Graph Learning: New Architecture and Unified ...

Efficient and interpretable graph network representation for angle ...

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and ...

Pre-training Interpretable Graph Neural Networks - NIPS papers

However, current interpretable GNNs mostly are dataset-specific and hard to generalize to different graphs. A more generalizable GNN interpretation model which ...

INTERPRETABLE GRAPH NEURAL NETWORKS FOR TABULAR ...

We propose an approach, called IGNNet (Interpretable Graph Neural. Network for tabular data), which constrains the learning algorithm to produce an.

naganandy/graph-based-deep-learning-literature - GitHub

Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks ... PRES: Toward Scalable Memory-Based Dynamic Graph Neural ...

Scalability and Interpretability of Graph Neural Networks for Small ...

most interpretable compared to the other graph neural networks. Algorithm 1 Learning Molecular Fingerprints. 1: for L = 1 to R do. 2: for ...

Graph Neural Networks – ESE 5140

GNNS are the tool for enabling scalable learning for signals supported on graphs. How to use this site (I am a student at Penn). If you are a student at Penn, ...

Scalable Graph Neural Networks with Deep Graph Library

The initial learning rate was set to 0.005 and the model was trained for 100 epochs with a dropout rate of 0.5 to prevent overfitting.

SDG: A Simplified and Dynamic Graph Neural Network - Dongqi Fu

Therefore, we make the first attempt to simplify and dynamize the structures of. GNNs for the scalability and interpretability, which is different from the ...

Interpretable Graph Convolutional Neural Networks for Inference on ...

A regularized attention mechanism to GCNNs is introduced that not only improves performance on clean datasets, but also favorably accommodates noise in KGs, ...