「GNN」の検索結果
17 件Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications re
Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure clas
Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, rea
The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership i
We introduce WEECFP, a parameter-free 1024-dimensional continuous molecular fingerprint that scatters each Mor
Graph coarsening reduces the large Quadratic Unconstrained Binary Optimization (QUBO) formulations arising whe
Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data
We present a unified reinforcement-learning (RL) framework that discovers compact parametrized quantum circuit
Urban air quality can vary significantly along transit corridors, necessitating high-resolution monitoring. Th
Learning rich medical concept representations is essential for EHR prediction. Text-attributed knowledge graph
Large-scale logistics networks require synthetic data generation capabilities to support scenario-based planni
Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the
As integrated circuit technology scales into the nanometer regime, the traditional disconnect between logic sy
Histopathological subtyping relies on the recognition of characteristic histological patterns. These patterns
Graph neural networks (GNNs) are a class of neural networks suitable for learning on graph-structured data. Th
Finding a representative description of graph entities that captures their structural roles and homophily is a
Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignment