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Computer Graphics

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Adaptive multi-graph contrastive learning for bundle recommendation.

Neural networks : the official journal of the International Neural Network Society
Recently, recommending bundles - sets of items that complement each other - instead of individual items to users has drawn much attention in both academia and industry. Models based on Graph Neural Networks (GNNs) for bundle recommendation have achie...

Graph Batch Coarsening framework for scalable graph neural networks.

Neural networks : the official journal of the International Neural Network Society
Due to the neighborhood explosion phenomenon, scaling up graph neural networks to large graphs remains a huge challenge. Various sampling-based mini-batch approaches, such as node-wise, layer-wise, and subgraph sampling, have been proposed to allevia...

Graph Curvature Flow-Based Masked Attention.

Journal of chemical information and modeling
Graph neural networks (GNNs) have revolutionized drug discovery in chemistry and biology, enhancing efficiency and reducing resource demands. However, classical GNNs often struggle to capture long-range dependencies due to challenges like oversmoothi...

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets.

IEEE transactions on visualization and computer graphics
Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial ob...

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-Based Synthetic Lethal Prediction.

IEEE transactions on visualization and computer graphics
Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for in...

DropNaE: Alleviating irregularity for large-scale graph representation learning.

Neural networks : the official journal of the International Neural Network Society
Large-scale graphs are prevalent in various real-world scenarios and can be effectively processed using Graph Neural Networks (GNNs) on GPUs to derive meaningful representations. However, the inherent irregularity found in real-world graphs poses cha...

A GPU-accelerated fuzzy method for real-time CT volume filtering.

PloS one
During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy images results in a higher radiation dose being administered to the patient. Filtering techniques can be...

An end-to-end bi-objective approach to deep graph partitioning.

Neural networks : the official journal of the International Neural Network Society
Graphs are ubiquitous in real-world applications, such as computation graphs and social networks. Partitioning large graphs into smaller, balanced partitions is often essential, with the bi-objective graph partitioning problem aiming to minimize both...

Graph explicit pooling for graph-level representation learning.

Neural networks : the official journal of the International Neural Network Society
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages: selecting top-ranked nodes and discard...

BAB-GSL: Using Bayesian influence with attention mechanism to optimize graph structure in basic views.

Neural networks : the official journal of the International Neural Network Society
In recent years, Graph Neural Networks (GNNs) have garnered significant attention, with a notable focus on Graph Structure Learning (GSL), a branch dedicated to optimizing graph structures to enhance network training performance. Current GSL methods ...