Search Results for author:
Found 10 papers, 2 papers with code
HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction
To address these challenges, we effectively model the interconnectedness of all entities in a heterogeneous graph and develop a novel Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}).
Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli.
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation
Further, we present a structure-aware self-attention mechanism that discovers the important nodes and hyperedges from both semantic and structural viewpoints.
MPool: Motif-Based Graph Pooling
Graph Neural networks (GNNs) have recently become a powerful technique for many graph-related tasks including graph classification.
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network
To address these challenges in the sequence classification problems, we propose a novel Hypergraph Attention Network model, namely Seq-HyGAN.
DDI Prediction via Heterogeneous Graph Attention Networks
Then, we develop a heterogeneous graph attention network to learn DDIs using relations of drugs with other entities.
HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network
However, these models have a high chance of failure, especially for the new drugs when all the information is not available.
Hypergraph Laplacians in Diffusion Framework
Networks are important structures used to model complex systems where interactions take place.
Social and Information Networks Spectral Theory 55U10, 91D30
Network Embedding: on Compression and Learning
For the first problem, we propose a neighborhood similarity based graph compression method that compresses the input graph to get a smaller graph without losing any/much information about the global structure of the graph and the local proximity of the vertices in the graph.
Opinion Mining on Non-English Short Text
We focus on a non-English language that has few resources for text mining.
