109 citations · 196 across the 30 of their papers we have counts for
11 papers · 1 filter
Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records
Tingyi Wanyan, Hossein Honarvar, Ariful Azad +2
Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions.…
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks
Md. Khaledur Rahman, Majedul Haque Sujon, Ariful Azad
We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparse-dense matrix multiplication under a single operation called FusedM…
Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale
Md Taufique Hussain, Oguz Selvitopi, Aydin Buluç +1
Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. In this paper, we consider SpGEMMs perf…
SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting
Langshi Chen, Jiayu Li, Ariful Azad +7
Subgraph counting aims to count occurrences of a template T in a given network G(V, E). It is a powerful graph analysis tool and has found real-world applications in diverse domain…
Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices
Oguz Selvitopi, Saliya Ekanayake, Giulia Guidi +3
Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein gra…
Force2Vec: Parallel force-directed graph embedding
Md. Khaledur Rahman, Majedul Haque Sujon, Ariful Azad
A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph. While graph embedding is fundamental…