Exploiting Edge Features for Graph Neural Networks

LightGCAN: A lightweight graph convolutional attention network for user preference modeling and personalized recommendation

Drug response prediction is an essential part of precision medicine, which assesses the efficacy of drugs in specific cancer cell lines. Recently, numerous deep learning models have been developed for drug response prediction. The ability to effectively represent complicated interactions within a network makes Graph Neural Network (GNN) models a prominent area of exploration. To exploit the diverse capabilities of different GNN models, the proposed model uses an ensemble approach that combines results from Graph Convolutional Network (GCN), Graph SAmple and aggreGatE Network (GraphSAGE) and Graph Attention Network (GAT)-based models for drug response prediction. The proposed ensemble GNN model is used to predict the efficacy of drugs in treating leukemia. The model outperformed individual models when evaluated with the GDSC1000 dataset, with lower root mean squared error, higher Pearson’s correlation coefficient, higher coefficient of determination and higher Spearman’s correlation coefficient. Furthermore, most of the drugs identified by the model as potential candidates for the treatment of leukemia are currently in clinical trials or have supporting evidence in the PubMed literature database.

PurposeDrug-induced liver injury (DILI) is a significant concern in drug development, often leading to the discontinuation of clinical trials and the withdrawal of drugs from the market. This study explores the application of graph neural networks (GNNs) for DILI prediction, using molecular graph representations as the primary input.MethodsWe evaluated several GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and Aggregation (GraphSAGE), and Graph Isomorphism Networks (GINs), using the latest FDA DILI dataset and other molecular property prediction datasets. We introduce a novel approach that creates a custom graph dataset, driven by molecular optimisation, that incorporates detailed and realistic chemical features such as bond lengths and partial charges as input into the GNN models. We have named our model approach DILIGeNN.ResultsDILIGeNN achieved an AUC of 0.897 on the DILI dataset, surpassing the current state-of-the-art model in the DILI prediction task. Furthermore, DILIGeNN outperformed the state-of-the-art in other graph-based molecular prediction tasks, achieving an AUC of 0.918 on the Clintox dataset, 0.993 on the BBBP dataset, and 0.953 on the BACE dataset, indicating strong generalisation and performance across different datasets.ConclusionDILIGeNN, utilising a single graph representation as input, outperforms the state-of-the-art methods in DILI prediction that incorporate both molecular fingerprint and graph-structured data. These findings highlight the effectiveness of our molecular graph generation and the GNN training approach as a powerful tool for early-stage drug development and drug repurposing pipeline.Scientific Contribution: DILIGeNN is a GNN framework that extracts graph features from 3D optimised molecular structures as is done in target-based drug discovery and molecular docking simulation. Our method is the first to encode spatial and electrostatic information into a single graph representation, as opposed to other work that require multiple graphs or additional chemical descriptors for feature representation. Our approach, using warm starts following repeated early stopping during training, outperforms the current state-of-the-art methods in liver toxicity (DILI), permeability (BBBP) and activity (BACE) prediction tasks.Graphic

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs are often noisy and incomplete. As a result, graph completion tasks, such as node classification or link prediction, have gained attention. On one hand, neural methods, such as graph neural networks, have proven to be robust tools for learning rich representations of noisy graphs. On the other hand, symbolic methods enable exact reasoning on graphs. We propose Knowledge Enhanced Graph Neural Networks (KeGNN), a neuro-symbolic framework for graph completion that combines both paradigms as it allows for the integration of prior knowledge into a graph neural network model. Essentially, KeGNN consists of a graph neural network as a base upon which knowledge enhancement layers are stacked with the goal of refining predictions with respect to prior knowledge. We instantiate KeGNN in conjunction with two well-known graph neural networks, Graph Convolutional Networks and Graph Attention Networks, and evaluate KeGNN on multiple benchmark datasets for node classification.

Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), specifically graph convolutional networks (GCNs), have gained popularity lately for their effectiveness in learning on graph data, achieving strong performance in adult sex classification tasks, their application to pediatric populations remains unexplored. We seek to characterize the capacity for GNN models to learn connectomic patterns on pediatric data through an exploration of training techniques and architectural design choices. Two datasets comprising an adult BRIGHT dataset (N = 147 Hodgkin's lymphoma survivors and N = 162 age similar controls) and a pediatric Human Connectome Project in Development (HCP-D) dataset (N = 135 healthy subjects) were utilized. Two GNN models (GCN simple and GCN residual), a deep neural network (multi-layer perceptron), and two standard machine learning models (random forest and support vector machine) were trained. Architecture exploration experiments were conducted to evaluate the impact of network depth, pooling techniques, and skip connections on the ability of GNN models to capture connectomic patterns. Models were assessed across a range of metrics including accuracy, AUC score, and adversarial robustness. GNNs outperformed other models across both populations. Notably, adult GNN models achieved 85.1% accuracy in sex classification on unseen adult participants, consistent with prior studies. The extension of the adult models to the pediatric dataset and training on the smaller pediatric dataset were sub-optimal in their performance. Using adult data to augment pediatric models, the best GNN achieved comparable accuracy across unseen pediatric (83.0%) and adult (81.3%) participants. Adversarial sensitivity experiments showed that the simple GCN remained the most robust to perturbations, followed by the multi-layer perceptron and the residual GCN. These findings underscore the potential of GNNs in advancing our understanding of sex-specific neurological development and disorders and highlight the importance of data augmentation in overcoming challenges associated with small pediatric datasets. Further, they highlight relevant tradeoffs in the design landscape of connectomic GNNs. For example, while the simpler GNN model tested exhibits marginally worse accuracy and AUC scores in comparison to the more complex residual GNN, it demonstrates a higher degree of adversarial robustness.

Accurately predicting the time aircraft spend within Terminal Maneuvering Areas (TMA) is crucial for optimizing air traffic management, minimizing delays, and improving operational efficiency. This study introduces two Graph Neural Network (GNN) models—Graph Convolutional Network (GCN) and Graph Attention Network (GAT)—to forecast aircraft TMA duration based on historical Automatic Dependent Surveillance-Broadcast (ADS-B) data collected from Istanbul TMA. The models leverage spatial-temporal patterns and inter-aircraft relationships to generate predictions. The GAT model employs an attention mechanism to assign varying importance to different nodes, dynamically capturing complex relationships within the TMA. In contrast, the GCN model applies uniform weighting to node connections, offering computational efficiency but limited adaptability to dynamic air traffic conditions. Results demonstrate that the GNN-based approach is effective for TMA duration prediction due to its ability to adapt to traffic density variations. This work underscores the potential of GNN-based approaches in air traffic management applications, facilitating better-informed decision-making processes for traffic controllers and operational planners.

Graph neural networks (GNNs) is an information - processing system that uses message passing among graph nodes. In recent years, GNN variants including graph attention network (GAT), graph convolutional network (GCN), and graph recurrent network (GRN) have shown revolutionary performance in computer vision applications using deep learning and artificial intelligence. These neural network model extensions, collect information in the form of graphs. GNN may be divided into three groups based on the challenges it solves: link prediction, node classification, graph classification. Machines can differentiate and recognise objects in image and video using standard CNNs. Extensive amount of research work needs to be done before robots can have same visual intuition as humans. GNN architectures, on the other hand, may be used to solve various image categorization and video challenges. The number of GNN applications in computer vision not limited, continues to expand. Human-object interaction, actin understanding, image categorization from a few shots and many more. In this paper use of GNN in image and video understanding, design aspects, architecture, applications and implementation challenges towards computer vision is described. GNN is a strong tool for analysing graph data and is still a relatively active area that needs further researches attention to solve many computer vision applications.

<title>Abstract</title> Rational vaccine design against emerging and understudied pathogens is hindered by incomplete protein–protein interaction (PPI) maps and limited integration of computational prioritization with network-based inference. Here, we present a unified framework that couples Biomni, a biomedical AI agent providing vaccine-priority and virulence-aware annotations, with graph neural network (GNN) based link prediction on STRING-derived PPIs to systematically identify candidate vaccine targets in Flavobacterium. Starting from the complete Flavobacterium proteome, Biomni-derived antigenicity, subcellular, and virulence features were integrated with high-confidence STRING associations into a unified PPI graph in which isolated and weakly connected proteins were explicitly retained. We evaluated three representative GNN architectures Graph Convolutional Network (GCN), Graph Attention Network (GAT), and GraphSAGE under a consistent three-fold cross-validation scheme and performed priorityaware assessment of predicted high-confidence links (posterior probability ≥ 0.90). GraphSAGE achieved the highest best-fold ROC-AUC (0.5585), followed by GAT (0.5536) and GCN (0.5000), reflecting modest yet meaningful discriminative performance in this sparse, single-species setting. Class-pair analyses revealed that GCN predominantly reconstructed links among lower-priority nodes, while GAT modestly increased coverage of interactions involving High-priority proteins but 1 remained biased toward low-tier combinations. In contrast, GraphSAGE produced a more balanced distribution of predicted links across Biomni priority tiers, including enriched High–Medium and High–Low connections, indicating more effective use of antigenicity and virulence features for inductive generalization to under-characterized proteins. Collectively, these results demonstrate that integrating AI-driven prioritization with inductive GNNs enables biologically informed exploration of missing PPIs and highlights previously overlooked Flavobacterium proteins as plausible vaccine candidates. The proposed Biomni–STRING–GNN framework is modular and transferable, offering a principled template for priority-aware, network-based vaccine target discovery in data-sparse pathogen systems.

Technology CAD (TCAD) tools are pivotal for transistor modeling, enabling physics-based simulations with high accuracy essential for developing next-generation technology nodes. However, their high computational cost and low throughput severely constrain large-scale design-space exploration. Accelerating TCAD simulations while preserving physical accuracy has therefore become a central research objective. A promising approach involves the use of graph-based representations of TCAD meshes fed into graph neural networks (GNNs), which offer rapid and accurate alternatives. However, traditional GNNs, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) often falter with noisy transistor-mesh graphs, where embeddings become cluttered and complex. In thiswork, we propose a self-supervisedGNNframework for TCAD device modeling, specifically employing the Self-Supervised GAT (SuperGAT).We demonstrate that self-supervised GNN models, in contrast to GCNs and GATs, introduces a self-supervised edge-prediction task to distinguish informative from redundant topological connections. Our evaluation compares various GNN aggregation schemes demonstrates that SuperGAT successfully reproduces key transistor transfer characteristics drain current–gate voltage (Id–Vg) curves, subthreshold swing (SS) and threshold voltage (Vth). The dataset was generated using 14nm FDSOI transistors. Our SuperGAT model achieves a minimum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.992 across all characteristics and an inference time of only 0.007 134 sec per transistor, resulting in a speed-up exceeding 100 000 x compared to TCAD simulation, which underscore the capability to substantially accelerate TCAD transistor simulations in advanced technology development.

Summary Monitoring carbon dioxide (CO2) saturation plume movement and pressure buildup is critical for ensuring the environmental safety of geological carbon storage (GCS) projects. High-fidelity numerical simulations provide accurate modeling of CO2 plume dynamics, but they are often computationally intensive. Recent advancements in data-driven models have enabled the rapid prediction of CO2 plume movement. By leveraging available simulation data sets, these models offer a more efficient alternative without compromising accuracy. In this study, we adopt the U-Net enhanced graph convolutional neural network (UGCN) to predict the spatial and temporal evolution of CO2 plume saturation and pressure buildup in saline aquifers. Utilizing the U-Net architecture, which incorporates skip connections, enables UGCN to capture high-level features and fine-grained details concurrently. We have created physics-based numerical simulation models that account for both CO2 injection and post-injection periods. By employing the Latin-hypercube sampling method, we generated a diverse range of reservoir and decision parameters, resulting in a comprehensive simulation database. We trained and tested the UGCN model on two different data sets, including a 2D radial reservoir model (Scenario 1) and a realistic Society of Petroleum Engineers Comparative Solution Project b (SPE 11b, Scenario 2) model to train and validate the UGCN model. The performance of the UGCN model was compared with other standard graph neural networks (GNNs), such as graph convolution network (GCN) and graph attention network (GAT). Notably, the UGCN model demonstrated robust performances on the blind testing dataset, achieving an R2 score of 0.993 and 0.989 for saturation predictions in Scenarios 1 and 2, respectively. Similarly, for pressure buildup the new model achieved an R2 of 0.989 and 0.999 for Scenarios 1 and 2, respectively. These prediction results indicate the effectiveness of the trained models in predicting the temporal and spatial evolution of CO2 saturation and pressure buildup predictions. Moreover, the prediction central processing unit (CPU) time for the deep learning (DL) models is significantly lower (0.02 seconds per case) than the physics-based reservoir simulator (on average, 10–15 minutes per case). This underscores the capability of the proposed method to provide predictions as accurate as physics-based simulations while reducing substantial computational costs.

AI-Driven molecule generation and bioactivity prediction: A multi-model approach combining VAE, graph and language-based neural networks.

The dominant paradigm in Natural Language Processing (NLP) has traditionally relied on sequential modelling, often overlooking the rich, non-linear structural dependencies inherent in linguistic data. Graph Neural Networks (GNNs) offer a powerful alternative by modelling text as structured graphs, yet a systematic evaluation of their comparative efficacy across tasks of varying structural complexity remains under-explored. This paper presents a comprehensive comparative study of three prominent GNN architectures- Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph SAGE- applied to three distinct NLP tasks: Text Classification, Semantic Role Labelling (SRL), and Abstract Meaning Representation (AMR) Parsing. We construct task-specific graph topologies, utilizing heterogeneous corpus graphs for document classification and syntactic dependency trees for SRL and AMR. Our empirical results, derived from benchmarks on the 20 Newsgroups, CoNLL-2009, and AMR 2.0 datasets, reveal a clear correlation between architectural properties and task complexity. While GCNs provide a robust and efficient baseline for global text classification (Accuracy: 96.6%), they struggle with the fine-grained structural modelling required for semantic tasks. Conversely, GATs demonstrate superior performance on syntax-heavy tasks, achieving a Labelled F1 score of 85.8% on SRL and a Smatch score of 74.5 on AMR parsing, significantly outperforming the GCN baseline (82.5% and 71.2%, respectively). These findings suggest that the anisotropic aggregation capability of attention mechanisms is critical for capturing the nuanced, long-range dependencies in natural language, establishing GAT as the preferred architecture for structure-dependent NLP applications.

A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network

The study of graph neural networks has revealed that they can unleash new applications in a variety of disciplines using such a basic process that we cannot imagine in the context of other deep learning designs. Many limitations limit their expressiveness, and researchers are working to overcome them to fully exploit the power of graph data. There are a number of publications that explore graph neural networks (GNNs) restrictions and bottlenecks, but the common thread that runs through them all is that they can all be traced back to message passing, which is the key technique we use to train our graph models. We outline the general GNN design pipeline in this study as well as discuss solutions to the over-smoothing problem, categorize the solutions, and identify open challenges for further research. Abbreviations: CGNN: Continuous Graph Neural Networks; CNN: Convolution NeuralNetwork; DeGNN: Decomposition Graph Neural Network; DGN: Directional GraphNetworks; DGN: Differentiable Group Normalization; DL: Deep Learning; EGAI:Enhancing GNNs by a High-quality Aggregation of Beneficial Information; GAT: GraphAttention Network; GCN: Graph Convolutional Network; GDC: Graph Drop Connect; GDR: Group Distance Ratio; GNN: Graph Neural Network; GRAND: GraphRandom Neural Networks; IIG: Instance Information Gain; MAD: Man AverageDistance; PDE-GCN: Partial Differential Equations-GCN; PTDNet: ParameterizedTopological Denoising network; TDGNN: Tree Decomposition Graph NeuralNetwork;