Word Embedding Distribution Propagation Graph Network for Few-Shot Learning

Graph neural networks (GNNs) have been used to tackle the few-shot learning (FSL) problem and shown great potentials under the transductive setting. However under the inductive setting, existing GNN based methods are less competitive. This is because they use an instance GNN as a label propagation/classification module, which is jointly meta-learned with a feature embedding network. This design is problematic because the classifier needs to adapt quickly to new tasks while the embedding does not. To overcome this problem, in this paper we propose a novel hybrid GNN (HGNN) model consisting of two GNNs, an instance GNN and a prototype GNN. Instead of label propagation, they act as feature embedding adaptation modules for quick adaptation of the meta-learned feature embedding to new tasks. Importantly they are designed to deal with a fundamental yet often neglected challenge in FSL, that is, with only a handful of shots per class, any few-shot classifier would be sensitive to badly sampled shots which are either outliers or can cause inter-class distribution overlapping. Extensive experiments show that our HGNN obtains new state-of-the-art on three FSL benchmarks. The code and models are available at https://github.com/TianyuanYu/HGNN.

Few-shot learning aims to learn a classifier that classifies unseen classes well with limited labeled samples. Existing meta learning-based works, whether graph neural network or other baseline approaches in few-shot learning, has benefited from the meta-learning process with episodic tasks to enhance the generalization ability. However, the performance of meta-learning is greatly affected by the initial embedding network, due to the limited number of samples. In this paper, we propose a novel Multi-granularity Recurrent Attention Graph Neural Network (MRA-GNN), which employs Multi-granularity graph to achieve better generalization ability for few-shot learning. We first construct the Local Proposal Network (LPN) based on attention to generate local images from foreground images. The intra-cluster similarity and the inter-cluster dissimilarity are considered in the local images to generate discriminative features. Finally, we take the local images and original images as the input of multi-grained GNN models to perform classification. We evaluate our work by extensive comparisons with previous GNN approaches and other baseline methods on two benchmark datasets (i.e., miniImageNet and CUB). The experimental study on both of the supervised and semi-supervised few-shot image classification tasks demonstrates the proposed MRA-GNN significantly improves the performances and achieves the state-of-the-art results we know.

Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i.e., acquiring new knowledge and skills with little or even no demonstration. Most of the existing meta-learning methods are proposed to tackle few-shot learning problems such as image and text, in rather Euclidean domain. However, there are very few works applying meta-learning to non-Euclidean domains, and the recently proposed graph neural networks (GNNs) models do not perform effectively on graph few-shot learning problems. Towards this, we propose a novel graph meta-learning framework -- Meta-GNN -- to tackle the few-shot node classification problem in graph meta-learning settings. It obtains the prior knowledge of classifiers by training on many similar few-shot learning tasks and then classifies the nodes from new classes with only few labeled samples. Additionally, Meta-GNN is a general model that can be straightforwardly incorporated into any existing state-of-the-art GNN. Our experiments conducted on three benchmark datasets demonstrate that our proposed approach not only improves the node classification performance by a large margin on few-shot learning problems in meta-learning paradigm, but also learns a more general and flexible model for task adaption.

A multi-agent artificial intelligence (AI) model is developed to automate the discovery of new metallic alloys, integrating multimodal data and external knowledge, including insights from physics via atomistic simulations. The system consists of (a) large language models (LLMs) for tasks such as reasoning and planning, (b) AI agents with distinct roles collaborating dynamically, and (c) a newly developed graph neural network (GNN) model for rapid retrieval of physical properties. We chose the ternary NbMoTa body-centered-cubic alloy as our model system and developed the GNN to predict two fundamental materials properties: the Peierls barrier and the solute/screw dislocation interaction energy. Our GNN model efficiently predicts these properties, reducing reliance on costly brute-force calculations and alleviating the computational demands on the multi-agent system. By combining the predictive capabilities of GNNs with the collaborative intelligence of LLM-driven reasoning agents, the system autonomously explores vast alloy design spaces, identifies trends in atomic-scale properties, and predicts macroscale mechanical strength, as demonstrated by several computational experiments. This synergistic approach accelerates the discovery of advanced alloys and holds promise for broader applications in other complex systems, marking a step forward in automated materials discovery and design. Impact statement Traditional deep learning models, such as graph neural networks and convolutional neural networks, operate within the confines of their training data sets, making single-step inferences for regression or classification. Our work introduces a multi-agent strategy that transcends these limitations by integrating deep learning with reasoning and decision-making capabilities. This intelligent system actively interprets results, determines subsequent actions, and iteratively refines predictions, accelerating the materials design process. We demonstrate its effectiveness in exploring the vast compositional space of a ternary alloy, where the model dynamically solicits data, analyzes trends, generates visualizations, and derives insights into materials behavior. By enabling accurate predictions of key alloy characteristics, our approach advances the discovery of novel metallic systems and underscores the critical role of solid-solution alloying. More broadly, it represents a major step toward integrating artificial intelligence with scientific reasoning, moving closer to artificial general intelligence in engineering. This paradigm shift has profound implications for materials science, enabling more efficient, autonomous, and intelligent exploration of complex materials spaces. Graphical Abstract

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.

We investigate works under the propagation-based fake news detection domain, which recently seeks to improve performance through the use of Graph Neural Networks (GNNs). Generally, existing works argue that using GNNs can give results superior to what was obtained using classic graph-based methods. We agree with this argument given that GNNs are capable of gaining superior performance by leveraging node features. But we argue that existing works haven't identified the fact that the expressivity of GNNs is limited and bounded by node features. Existing works do not acknowledge that, by utilizing GNNs, they implicitly assume node features are strongly correlated to node labels. There are evidence that node features that have been employed do not necessarily correlate to node label. Instead of having a profound theoretical motivation, they have empirically observed that focusing on nodes features with strong feature-label correlation can increase predictive capability. This is a sub-optimal approach to view this problem, in fact, we argue that finding node features based on correlation is not practical or effective. Our first contribution is shifting readers from a node-level view i.e correlating node features with labels, to a graph-level view. In the graph-level view, we exploit the relationship between graph isomorphism and GNNs' expressivity which can be utilized to well understand and interpret the relation between node features and GNNs' expressivity. We conduct a wide range of experiments on basis of both node-level view and graph-level view and found graph-level view is more interpretable and strongly matches with results. Further, we gained insights on node features that wouldn't be obtainable by a node-level view. In order to have a fair and comprehensive analysis of node features, we built a unified dataset that includes a wide range of node features. Our results indicate, as we improve model accuracy on basis of the graph level view, models' generalizability decreases. We provide our hypothesis for this performance trade-off on the basis of the graph-level view. Our results and insights call for a much broader discussion on whether any sort of filtering method is effective. So, we conclude our work by providing readers with possible solutions that can be helpful to find harmony between node features and GNNs' expressivity.

Few-shot learning is a method to acquire learning ability in a small amount of sample data scenarios. This paper aims to study an online and offline interaction model applied to international Chinese education and teaching based on the few-shot learning method. It first expounds an overview of international Chinese education, including the connotation and characteristics of the hybrid mode of online teaching and offline teaching in international Chinese education, then designs an online and offline interactive model, and finally compares it with a baseline model and a traditional single teaching model. The experimental data shows that the accuracy and recall rate of the international Chinese education online and offline interaction model based on few-shot learning both reach 70%, which verifies its effectiveness.

In real-world applications, graph data has garnered significant attention for its representation and analysis using Graph Neural Networks. Recent advancements have led to the development of Graph Foundation Models (GFMs), which aim to enhance cross-domain and cross-task generalization ability. Despite promising results from GFMs, a lack of standardized evaluation processes hinders comparative analysis and cross-domain applicability. To address this gap, we propose GFMBench, an open-source pipeline that standardizes the training, evaluation, and deployment of GFMs across diverse real-world graph applications. GFMBench integrates state-of-the-art GFMs and datasets, providing a modular design for comprehensive support across data preprocessing, model training, and evaluation. The pipeline includes a robust evaluation framework for benchmarking GFM generalization ability, encompassing supervised learning, cross-domain zero-shot and few-shot learning, and in-context learning. To validate the usability of GFMs, we deploy them on the Open Academic Graph, enabling applications such as topic search and author recommendation. This work provides a unified benchmark for GFMs, enabling deeper insights into their generalization ability across various graph tasks and domains. We further open-source GFMBench https://github.com/BUPT-GAMMA/ggfm and related documents https://ggfm.readthedocs.io/en/latest/.

In recent years, smart contract vulnerability detection methods mostly view smart contract source code as natural language for processing, which cannot fully capture the semantic and structural features of the source code and has a high rate of false positives and missing positives. To improve the accuracy of vulnerability detection, this paper uses Graph Neural Network to obtain the semantic and structural information of the source code and Convolutional Neural Network to assist learning. We propose a graph neural network-based vulnerability detection model for smart contracts, which transforms smart contracts into control flow graphs, learns graph embedding using graph neural networks, and introduces Convolutional Neural Networks to learn the node order information of control flow graphs, and finally performs vulnerability detection using graph embedding and node order information. Experimenting on real datasets, our accuracy and F1 values are improved and the model can effectively detect smart contract vulnerabilities.

Exploring new horizons: Empowering computer-assisted drug design with few-shot learning

Introduction/Objective Recent advancements in artificial intelligence (AI), particularly with Large Language Models like ChatGPT-4Vision (GPT-4V), have expanded the potential for automated interpretation of medical images. This study evaluates the diagnostic accuracy of GPT-4V in histopathological analysis of neurodegenerative diseases and compares its performance with traditional Convolutional Neural Networks (CNNs). Methods/Case Report We utilized 1515 histopathological images, including hematoxylin and eosin (H&E) staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer’s disease, progressive supranuclear palsy, and corticobasal degeneration. GPT-4V’s performance was assessed through multi- step prompts to assess how textual context influences the image interpretation. In the quantitative analysis, the diagnostic accuracy of GPT-4V and the CNN-based model YOLOv8 was assessed for classifying three tau lesions—astrocytic plaques, neuritic plaques, and tufted astrocytes. The analysis used both zero-shot and few-shot learning methods, where GPT-4V and YOLOv8 were evaluated on their ability to distinguish these lesions using ten hold-out test images and 490 training images per lesion. Results (if a Case Study enter NA) GPT-4V accurately recognized Lewy bodies in all cases, but it showed limited accuracy in identifying neurofibrillary tangles (20%) and Pick bodies (0%) on H&E slides. Notably, GPT-4V often suggested Alzheimer’s disease as a potential diagnosis, relying more on contextual clues, such as the prevalence of certain diseases, rather than direct image analysis. In the tau immunohistochemical analysis, GPT-4V’s diagnoses were strongly influenced by additional information about staining and brain region. However, few-shot learning markedly improved GPT-4V’s diagnostic capabilities from 40% accuracy with zero-shot learning to 90% with few-shot learning, reaching best accuracy with 20-shot learning. This performance compared favorably against YOLOv8, which also achieved 90% accuracy but required 100-shot learning. Conclusion While GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its accuracy. This approach is particularly promising for neuropathology, where acquiring extensive labeled datasets is challenging. The findings highlight the need for ongoing refinement of AI applications in pathology, emphasizing balanced integration of textual and visual data to optimize diagnostic efficacy and reliability.

Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under standard test vectors, and the changes they bring into power consumption, timing, and area are minimal. We present a dual-domain feature extraction strategy that combines time-domain features with frequency-domain characteristics of power traces. For the detection, we created an artificial intelligence (AI) based robust Trojan detection framework that integrates traditional machine learning models, such as random forest (RF), gradient boosting (GB), naïve bayes (NB), and deep learning models, such as deep neural network (DNN), long short-term memory (LSTM), and graph neural network (GNN). In this study, we consider these models as baseline AI models to detect Trojan-infected circuits via side-channel power analysis. We employed a stacked ensemble classifier that integrates the distinct strengths of the six baseline models used in this study. After evaluating our stacking ensemble-based detection method on the advanced encryption standard (AES)-Trojan benchmark, which covers diverse Trojan types, the results demonstrate that the ensemble method consistently outperformed all six baseline models. The ensemble-based detection method achieved a macro-averaged area under the receiver operating characteristic (ROC) curve (AUC) of 0.987, while remaining golden-chip-free, meaning it does not rely on a trusted reference IC for baseline comparison. Instead it detects anomalies directly from observable characteristics of untrusted chips, such as side-channel emissions.

In the era of information data explosion, accurately classifying massive text data and mining emotional tendencies play an important role in purifying the network environment. A sentiment classification model that combines the advantages of BERT and graph neural networks is proposed to address the shortcomings of a single traditional classification model in terms of classification accuracy and efficiency. It is recommended to combine the transformer based BERT model with graph neural networks, using the text embedding technology in BERT to leverage its powerful feature extraction ability and the advantage of graph neural networks in aggregating semantic features of text structure information. This will advance the semantic information of the entire text and then pass the semantic features to the combined model for classification. At the same time, in text sequence processing, it is proposed to construct an emotional dependency tree and obtain a leading matrix based on dependency relationships, which can improve the model's understanding of text semantic and structural information. The model was tested on English datasets such as 20NG and R8, as well as the Weibo 100k dataset, with a particularly high rate of 93.7% on the Weibo 100k dataset. Comparative experiments were conducted with other models.The results indicate that compared with other segmentation models, our model has better segmentation performance and can effectively aggregate semantic information of comment texts.

Deep learning has made tremendous success in computer vision, natural language processing and even visual-semantic learning, which requires a huge amount of labeled training data. Nevertheless, the goal of human-level intelligence is to enable a model to quickly obtain an in-depth understanding given a small number of samples, especially with heterogeneity in the multi-modal scenarios such as visual question answering and image captioning. In this paper, we study the few-shot visual-semantic learning and present the Hierarchical Graph ATtention network (HGAT). This two-stage network models the intra- and inter-modal relationships with limited image-text samples. The main contributions of HGAT can be summarized as follows: 1) it sheds light on tackling few-shot multi-modal learning problems, which focuses primarily, but not exclusively on visual and semantic modalities, through better exploitation of the intra-relationship of each modality and an attention-based co-learning framework between modalities using a hierarchical graph-based architecture; 2) it achieves superior performance on both visual question answering and image captioning in the few-shot setting; 3) it can be easily extended to the semi-supervised setting where image-text samples are partially unlabeled. We show via extensive experiments that HGAT delivers state-of-the-art performance on three widely-used benchmarks of two visual-semantic learning tasks.

Few-shot learning remains a challenging problem, with unsatisfactory 1-shot accuracies for most real-world data. Here, we present a new perspective for data distributions in the feature space of a deep network and show how to exploit this perspective for few-shot learning. First, we observe that nearest neighbors in the feature space are with high probability members of the same class while generally two random points from one class are not much closer to each other than two points between classes. This observation suggests that classes in feature space form sparse, loosely connected graphs instead of dense clusters. To exploit this property, we propose using label propagation to the nearest unlabeled data and then using a kernel PCA reconstruction error as decision boundary in feature-space for the data distribution of each class. Using this method, which we call "K-Prop," we demonstrate largely improved few-shot learning performances (e.g., 83% accuracy for 1-shot 5-way classification on the RESISC45 satellite-images dataset) for datasets for which a backbone network can be trained to produce high within-class nearest-neighbor probabilities. We demonstrate this relationship using six different datasets.