This paper investigates a novel and critical problem of Graph Continual Test-Time Training, which aims to enable a frozen pre-trained graph model to adapt continuously to evolving out-of-distribution (OOD) graphs without supervision. Existing test-time training methods primarily focus on one-step adaptation and overlook long-term knowledge retention, while conventional continual learning approaches rely on labeled data and static memory replay. Consequently, they are unable to handle sequential OOD domains effectively, often suffering from severe forgetting and limited efficiency in dynamic graph environments. To address these challenges, we propose DPCGL (Dynamic Prompts-based Continual Graph Learning), a data-centric framework that performs continual test-time training through adaptive prompt optimization. DPCGL freezes the pre-trained backbone and maintains a dynamic prompt pool, where prompts are adaptively selected and updated for each incoming graph domain. This design enables parameter-efficient adaptation and mitigates forgetting by organizing transferable knowledge within prompts rather than model weights. Furthermore, DPCGL jointly optimizes three objectives: similarity alignment for representativeness, KL divergence regularization for knowledge preservation, and diversity constraint for generalization, providing both stability and adaptability during continual adaptation. Extensive experiments on multiple evolving OOD graph benchmarks demonstrate that DPCGL achieves state-of-the-art performance, effectively alleviating catastrophic forgetting and enabling robust continual adaptation across domains.

Graph Neural Networks (GNNs) have been widely used across various fields under the homophily assumption that connected nodes are similar. However, in heterophilic graphs, where connected nodes tend to have dissimilar features, existing GNNs still face some limitations. From the perspective of structure, shallow GNNs could not capture the high-order node information, whereas deep GNNs may suffer from the over-smoothing problem. From the perspective of feature, the useful information of high-order similar nodes is often weakened by low-order dissimilar nodes in the feature update phase. To address the above problems, we propose a Global Structure-aware and Feature-augmented Graph Neural Network (GSF-GNN) to alleviate the limitations from the perspectives of structure and feature. Specifically, from the structure perspective, we design a Structure-based Global Propagation (SGP) module to establish global connections among nodes and adaptively adjust edge weights for message propagation. From the feature perspective, we introduce a Feature-augmented Compensatory Update (FCU) module, which employs a multi-view feature updating mechanism to enhance node features from different perspectives. Our theoretical analysis formally demonstrates the effectiveness of GSF-GNN in heterophilic graphs. Experiments on heterophilic and homophilic benchmark datasets validate the effectiveness of GSF-GNN across various graph structures. Moreover, GSF-GNN achieves stable performance across multiple layers and effectively alleviates the over-smoothing problem. Our codes are available on https://github.com/huijieliu2023/GSF-GNN .

As more and more internet users post images online to express their daily emotions, image sentiment analysis has attracted increasing attention. Recently, researchers generally tend to design different neural networks to extract visual features from images for sentiment analysis. Despite the significant progress, metadata, the data (e.g., text descriptions and keyword tags) for describing the image, has not been sufficiently explored in this task. In this paper, we propose a novel Metadata Enhanced Transformer for sentiment analysis (SentiFormer) to fuse multiple metadata and the corresponding image into a unified framework. Specifically, we first obtain multiple metadata of the image and unify the representations of diverse data. To adaptively learn the appropriate weights for each metadata, we then design an adaptive relevance learning module to highlight more effective information while suppressing weaker ones. Moreover, we further develop a cross-modal fusion module to fuse the adaptively learned representations and make the final prediction. Extensive experiments on three publicly available datasets demonstrate the superiority and rationality of our proposed method.

Knowledge combination prediction involves analyzing current knowledge elements and their relationships, then forecasting how these elements, drawn from various fields, can be creatively combined to form new, innovative solutions. This process is critical for countries and businesses to understand future technology trends and promote innovation in an era of rapid scientific and technological advancement. Existing methods often overlook the integration of knowledge combinations from multiple views, along with their inherent heterophily and the dual “many-to-one” property, where a single knowledge combination can include multiple elements, and a single element may belong to various combinations. To this end, we propose a novel framework named Multi-viewHeterogeneousHyperGNN forHeterophilicKnowledgeCombinationPrediction (H3KCP). Specifically, H3KCP first constructs a hypergraph reflecting the dual “many-to-one” property of knowledge combinations, where each hyperedge may contain several nodes and each node can also belong to multiple hyperedges. Next, the framework employs a multi-view fusion approach to model knowledge combinations, considering heterophily and integrating insights from co-occurrence, co-citation, and hierarchical structure-based views. Furthermore, our analysis of H3KCP from a spectral graph perspective offers insights into its rationality. Finally, extensive experiments on real-world patent datasets and the Open Academic Graph dataset validate the effectiveness and efficiency of our approach, yielding significant insights into knowledge combinations. Our code and dataset are publicly available onhttps://github.com/huijieliu2023/H3KCP.

Programmatic Weak Supervision (PWS) is a recent data labeling paradigm, which employs several Labeling Functions (LFs) to provide weak labels and involves a Label Model (LM) for label aggregation. Despite the significant progress, there still remain some inherent challenges in PWS. From the view of labeling, LFs may wrongly label some data points. From the view of data, some data points themselves may be low-quality (e.g., ambiguous texts or blurred images). These largely stem from the lack of an explicit evaluation mechanism for LFs or data points. To this end, inspired by confident learning focusing on label quality, we propose a Confident PWS (CPWS) approach for high-quality data labeling. Specifically, several LFs are firstly utilized to provide weak labels for unlabeled data. Then, we develop an explicit Dual Evaluation Mechanism (DEM) to evaluate the quality of both LFs and data points, which not only employs data to evaluate trained models but also leverages trained models to evaluate data. Along this line, we further design a Distribution-Guided Pruning Strategy (DPS) to prune low-quality data and aggregate weak labels under the guidance of label class distribution. Extensive experiments on various benchmark datasets demonstrate the effectiveness and generalization ability of our proposed approach.

With the accelerated technology development, technological trend forecasting through patent mining has become a hot issue for high-tech companies. In this term, extensive attention has been attracted to forecasting technological knowledge flows (TKF), i.e., predicting the directional flows of knowledge from one technological field to another. However, existing studies either rely on labor intensive empirical analysis or do not consider the intrinsic characteristics inherent in TKF, including the double-faced aspects (i.e., act as both the source and target) of technology nodes, multiple complex relationships among different technologies, and dynamics of the TKF process. To this end, in this paper, we make a further study and propose a data-driven solution, i.e., a Hierarchical Interactive Graph Neural Network (HighTKF), to automatically find the potential flow trends of technologies. Specifically, HighTKF makes final predictions through two kinds of representations of each technology node (a diffusion vector and an absorption vector), which is realized by three components: High-Order Interaction Module (HOI), Hierarchical Delivery Module (HD) and Technology Flow Tracing Module (TFT). For one thing, HOI and HD aim to model high-order network relationships and hierarchical relationships among technologies. For another, TFT is designed for capturing the dynamic feature evolution of technologies with the above relations involved. Also, we design a hybrid loss function and propose a new evaluation metric for better predicting the unprecedented flows between technologies. Finally, we conduct extensive experiments on a real-world patent dataset, the results verify the effectiveness of our approach and reveal some interesting phenomenons on technological knowledge flow trends.