8 citations · 19 across the 12 of their papers we have counts for
6 papers · 1 filter
MarkovGNN: Graph Neural Networks on Markov Diffusion
Md. Khaledur Rahman, Abhigya Agrawal, Ariful Azad
Most real-world networks contain well-defined community structures where nodes are densely connected internally within communities. To learn from these networks, we develop MarkovG…
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed
Nicholas Majeske, Bidisha Abesh, Chen Zhu +1
We present a machine learning method to predict extreme hydrologic events from spatially and temporally varying hydrological and meteorological data. We used a timestep reduction t…
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Tingyi Wanyan, Jing Zhang, Ying Ding +3
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events. These tasks, however, o…
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…
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN
Tingyi Wanyan, Chenwei Zhang, Ariful Azad +3
We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task. It uses multiple local GCN filters to do feature extraction in every propa…