Graph neural networks (GNNs) are mathematical models that can learn functions over graphs and are a leading approach for building predictive models on graph-structured data. This combination has enabled GNNs to advance the state of the art in many disciplines, from discovering new antibiotics and ...
Graphs are flexible mathematical objects that can represent many entities and knowledge from different domains, including in the life sciences. Graph neural networks (GNNs) are mathematical models that can learn functions over graphs and are a leading ap
Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks. Briefings in Bioinformatics. 2023;24(6):bbad414–bbad414. Article Google Scholar Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali T. Benchmarking algorithms for gene regulatory network inference ...
et al. summarizing the success of ML and deep neural networks on omics data, many graph-based methods remain unreviewed and unsystematized (Zhang et al.2019b; Li et al.2022a). To close these gaps, this paper thoroughly reviews current global and hierarchical global pooling operators and su...
GraphNeural Networksexploit all the information available at each step. • The sequential and modular nature of our method makes it interpretable. • Each module is trained independently, simplifying learning and optimization. • The model is evaluated on the QM9 and Zinc benchmark datasets of...
Meanwhile, graph neural networks (GNNs) are a particular type of deep neural network that are interpretable and flexible. Their adaptability in solving complex problems in data analysis with a graph structure has made them one of the most efficient methods in the last decade. Considering the ...
Instead of single RNN, they propose using several parallel RNNs to model different nature of the input data respectively. Some works have focused on using convolution neural networks (CNN) for recommendations. Kim, Park, Oh, Lee, and Yu (2016) proposed incorporating auxiliary information from ...
Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground...
with them. In recent years, systems based on variants of graph neural networks such as graph convolutional network (GCN), graph attention network (GAT), gated graph neural network (GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a ...
From raw detector activations to reconstructed particles, data at the Large Hadron Collider (LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural networks (GNNs), a class of algorithms belonging to the rapidly growing field of geometric deep learning (GDL), are ...