Accurate Lung Cancer Prediction From CT Scans Using Advanced Deep Learning Methods.

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.

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

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 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.

Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural networks (DCNNs) has made accurate pixel-level classification tasks possible. Yet one central issue remains: the precise delineation of boundaries. Deep architectures generally fail to produce fine-grained segmentation with accurate boundaries due to their progressive down-sampling. Hence, we introduce a generic framework to overcome the issue, integrating the graph convolutional network (GCN) and deep structured feature embedding (DSFE) into an end-to-end workflow. Furthermore, instead of using a classic graph convolutional neural network, we propose a gated graph convolutional network, which enables the refinement of weak and coarse semantic predictions to generate sharp borders and fine-grained pixel-level classification. Taking the semantic segmentation of building footprints as a practical example, we compared different feature embedding architectures and graph neural networks. Our proposed framework with the new GCN architecture outperforms state-of-the-art approaches. Although our main task in this work is building footprint extraction, the proposed method can be generally applied to other binary or multi-label segmentation tasks.

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

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;

Traffic forecasting is a crucial aspect of modern Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV), playing a vital role in improving the safety and efficiency of daily transportation activities. Despite the valuable contributions of traditional machine learning (ML) models and advanced deep learning (DL) techniques, there persist challenges in capturing the intricate spatial and temporal dependencies inherent in traffic flow. In response to these challenges, we present GRAPHITE, an innovative framework that combines Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to leverage generative reasoning for efficient traffic management. Our model seamlessly integrates historical traffic volume data collected by road sensors with local spatial information encoded through knowledge graphs (KGs) associated with each sensor. These KGs offer a structured representation of relationships between traffic sensors and points of interest (POIs) in their neighborhood, thereby enhancing the comprehension of the urban context and leading to more accurate traffic predictions. Extensive experiments conducted on diverse datasets underscore the efficacy of GRAPHITE. Notably, we achieved a maximum decrease in RMSE of 31.05% compared to GAN-GRU and a maximum increase in R2 of 8.15% compared to GAN-RNN, positioning GRAPHITE as a standout solution among the current state-of-the-art approaches. Our code is available at: https://github.com/MODAL-UNINA/GRAPHITE.

In recent years, tremendous efforts have been made to explore features contained in user-item graphs for recommendation based on Graph Neural Networks (GNN). However, most existing recommendation methods based on GNN use weighted sum of directly-linked node’s features only, assuming that neighboring nodes are independent individuals, neglecting possible correlations between neighboring nodes, which may result in failure of capturing co-occurrence signals. Therefore, in this paper, we propose a novel Graph Convolutional Network with Neighbor complex Interactions for Recommendation (GCNNIRec) focused upon capturing possible co-occurrence signals between node neighbors. Specifically, two types of modules, the Linear-Aggregator module and the Interaction-Aggregator module are both inside GCNNIRec. The former module linearly aggregates the features of neighboring nodes to obtain the representation of target node. The latter utilizes the interactions between neighbors to aggregate the co-occurrence features of nodes to capture co-occurrence features. Furthermore, empirical results on three real datasets confirm not only the state-of-the-art performance of GCNNIRec but also the performance gains achieved by introducing Interaction-Aggregator module into GNN.

<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.

The generalization error of graph convolutional networks may enlarge with more layers

Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g. graph convolutional networks (GCN) and graph attention networks (GAT), adequately utilize edge features, especially multi-dimensional edge features. In this paper, we build a new framework for a family of new graph neural network models that can more sufficiently exploit edge features, including those of undirected or multi-dimensional edges. The proposed framework can consolidate current graph neural network models; e.g. graph convolutional networks (GCN) and graph attention networks (GAT). The proposed framework and new models have the following novelties: First, we propose to use doubly stochastic normalization of graph edge features instead of the commonly used row or symmetric normalization approches used in current graph neural networks. Second, we construct new formulas for the operations in each individual layer so that they can handle multi-dimensional edge features. Third, for the proposed new framework, edge features are adaptive across network layers. As a result, our proposed new framework and new models can exploit a rich source of graph information. We apply our new models to graph node classification on several citation networks, whole graph classification, and regression on several molecular datasets. Compared with the current state-of-the-art methods, i.e. GCNs and GAT, our models obtain better performance, which testify to the importance of exploiting edge features in graph neural networks.

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.

Graph Neural Networks (GNNs) have become a critical tool for learning on structured data, particularly for their ability to capture feature dependencies within multivariate datasets. This study investigates the application of Graph Convolutional Networks (GCNs) to improve classification performance on such data. We constructed a fully connected graph using cosine similarity to compare our GCN approach against standard dimensionality reduction techniques like PCA and t-SNE. In our tests, the GCN model clearly surpassed these baselines in accuracy, precision, recall, and F1-score.But the method has its own trade-offs: dense graphs are computationally costly, there are chances of overfitting on smaller datasets and the resulting embeddings may be uninterpretable. We cannot make such a claim of generality at this point because this review has only limited itself to one multivariate data set. In future endeavour we would wish to compare GCNs to more powerful classifiers, such as Random Forests, Deep Neural Networks, with greater regard to efficiency in training and resistance to noise. To summarize, this paper confirms that GNNs are an ideal option when it comes to multivariate classification, although scalability and interpretability have to be resolved in order to make it applicable in the real-life context.

Graph neural networks (GNNs) have recently gained much attention for node and graph classification tasks on graph-structured data. However, multiple recent works showed that an attacker can easily make GNNs predict incorrectly via perturbing the graph structure, i.e., adding or deleting edges in the graph. We aim to defend against such attacks via developing certifiably robust GNNs. Specifically, we prove the first certified robustness guarantee of any GNN for both node and graph classifications against structural perturbation. Moreover, we show that our certified robustness guarantee is tight. Our results are based on a recently proposed technique called randomized smoothing, which we extend to graph data. We also empirically evaluate our method for both node and graph classifications on multiple GNNs and multiple benchmark datasets. For instance, on the Cora dataset, Graph Convolutional Network with our randomized smoothing can achieve a certified accuracy of 0.49 when the attacker can arbitrarily add/delete at most 15 edges in the graph.