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Found 14 papers, 7 papers with code
iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations
The library offers a user-friendly Python plug-in that allows users to take advantage of our optimized PyTorch operations out-of-the-box for any existing linear algebra-based PyTorch implementation of popular GNNs (Graph Convolution Network, GraphSAGE, Graph Inference Network, etc.)
GNNShap: Scalable and Accurate GNN Explanation using Shapley Values
Game theoric Shapley value approaches are popular explanation methods in other domains but are not well-studied for graphs.
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy
Thus, without sacrificing accuracy, graph sparsification, or model compression becomes a viable approach for graph learning tasks.
MarkovGNN: Graph Neural Networks on Markov Diffusion
Most real-world networks contain well-defined community structures where nodes are densely connected internally within communities.
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods
The learned embeddings have been successfully applied to perform various prediction tasks, such as link prediction, node classification, clustering, and visualization.
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed
We present a machine learning method to predict extreme hydrologic events from spatially and temporally varying hydrological and meteorological data.
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events.
Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records
In this work, we train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and use the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality.
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks
We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparse-dense matrix multiplication under a single operation called FusedMM.
Force2Vec: Parallel force-directed graph embedding
A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph.
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN
We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task.
High-performance sparse matrix-matrix products on Intel KNL and multicore architectures
Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type.
Distributed, Parallel, and Cluster Computing
Integrated Model, Batch and Domain Parallelism in Training Neural Networks
We propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using minibatch stochastic gradient descent (SGD).
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data.
