Recent advances in generic large models, such as GPT and DeepSeek, have motivated the introduction of universality to graph pre-training, aiming to learn rich and generalizable knowledge across diverse domains using graph representations to improve performance in various downstream applications. However, most existing methods face challenges in learning effective knowledge from generic graphs, primarily due to simplistic data alignment and limited training guidance. The issue of simplistic data alignment arises from the use of a straightforward unification for highly diverse graph data, which fails to align semantics and misleads pre-training models. The problem with limited training guidance lies in the arbitrary application of in-domain pre-training paradigms to cross-domain scenarios. While it is effective in enhancing discriminative representation in one data space, it struggles to capture effective knowledge from many graphs. To address these challenges, we propose a novel Latent sEmantic Distribution Alignment (LEDA) model for universal graph pre-training. Specifically, we first introduce a dimension projection unit to adaptively align diverse domain features into a shared semantic space with minimal information loss. Furthermore, we design a variational semantic inference module to obtain the shared latent distribution. The distribution is then adopted to guide the domain projection, aligning it with shared semantics across domains and ensuring cross-domain semantic learning. LEDA exhibits strong performance across a broad range of graphs and downstream tasks. Remarkably, in few-shot cross-domain settings, it significantly outperforms in-domain baselines and advanced universal pre-training models.

The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.

Multivariate time series forecasting holds significant theoretical and practical importance in various fields, including web analytics and transportation. Recently, graph neural networks and graph differential equations have shown exceptional capabilities in modeling spatio-temporal features. However, existing methods often suffer from over-smoothing, hindering real-world problem-solving. In this work, we analyze the graph propagation process as a dynamical system and propose a novel feedback mechanism to enhance representation power, adaptively adjusting the representations to align with desired performance outcomes, thereby fundamentally mitigating the issue of over-smoothing. Moreover, we introduce an effective multivariate time series forecasting model called SF-GDE, based on the proposed graph propagation with the feedback mechanism. Intensive experiments are conducted on three real-world datasets from diverse fields. Results show that SF-GDE outperforms the state of the arts, and the feedback mechanism can serve as a universal booster to improve performance for graph propagation models.

With advancements in deep learning, neural simulators have become increasingly important for improving the efficiency and effectiveness of simulating complex dynamical systems in various scientific and technological fields. This paper presents a novel neural simulator called Context-informed Polymorphic Neural ODE Processes (CoPoNDP), aimed at addressing the challenges of modeling dynamical systems encountering concurrent environmental and temporal distribution shifts, which are common in real-world scenarios. CoPoNDP employs a context-driven neural stochastic process governed by a combination of basic differential equations in a time-sensitive manner to adaptively modulate the evolution of system states. This allows for flexible adaptation to changing temporal dynamics and generalization across different environments. Extensive experiments conducted on dynamical systems from ecology, chemistry, physics, and energy demonstrate that by effectively utilizing contextual information, CoPoNDP outperforms the state-of-the-art models in handling joint distribution shifts. It also shows robustness in sparse and noisy settings, making it a promising approach for modeling dynamical systems in complex real-world applications.

Single-cell RNA sequencing (scRNA-seq) reveals cell heterogeneity, with cell clustering playing a key role in identifying cell types and marker genes. Recent advances, especially graph neural networks (GNNs)-based methods, have significantly improved clustering performance. However, the analysis of scRNA-seq data remains challenging due to noise, sparsity, and high dimensionality. Compounding these challenges, GNNs often suffer from over-smoothing, limiting their ability to capture complex biological information. In response, we propose scSiameseClu, a novel Siamese Clustering framework for interpreting single-cell RNA-seq data, comprising of 3 key steps: (1) Dual Augmentation Module, which applies biologically informed perturbations to the gene expression matrix and cell graph relationships to enhance representation robustness; (2) Siamese Fusion Module, which combines cross-correlation refinement and adaptive information fusion to capture complex cellular relationships while mitigating over-smoothing; and (3) Optimal Transport Clustering, which utilizes Sinkhorn distance to efficiently align cluster assignments with predefined proportions while maintaining balance. Comprehensive evaluations on seven real-world datasets demonstrate that scSiameseClu outperforms state-of-the-art methods in single-cell clustering, cell type annotation, and cell type classification, providing a powerful tool for scRNA-seq data interpretation.

Fault Scenario Identification (FSI) is a challenging task that aims to automatically identify the fault types in communication networks from massive alarms to guarantee effective fault recoveries. Existing methods are developed based on rules, which are not accurate enough due to the mismatching issue. In this paper, we propose an effective method named Knowledge-Enhanced Graph Neural Network (KE-GNN), the main idea of which is to integrate the advantages of both the rules and GNN. This work is the first work that employs GNN and rules to tackle the FSI task. Specifically, we encode knowledge using propositional logic and map them into a knowledge space. Then, we elaborately design a teacher-student scheme to minimize the distance between the knowledge embedding and the prediction of GNN, integrating knowledge and enhancing the GNN. To validate the performance of the proposed method, we collected and labeled three real-world 5G fault scenario datasets. Extensive evaluation conducted on these datasets indicates that our method achieves the best performance compared with other representative methods, improving the accuracy by up to 8.10%. Furthermore, the proposed method achieves the best performance against a small dataset setting and can be effectively applied to a new carrier site with a different topology structure.

With the rapid development of mobile networks and the ubiquity of mobile devices, spatial crowdsourcing (SC), which refers to assigning spatial–temporal tasks to moving workers, has drawn increasing attention. Thus, many researchers aim at various task assignment methods in SC. However, existing works generally consider workers’ location and preference categories separately. Ignorance of the correlation between them can often lead to poor assignment results. In this article, we propose a location-and-preference joint prediction model (JPM) to predict workers’ locations and preference categories jointly at each sample timestamp. Based on the predictive location probability distribution and preference probability distribution, we elaborately design a greedy multiattribute joint task assignment algorithm (MAJA) to maximize the average number of completed tasks under constraints. Then, an overall procedure incorporating the JPM and MAJA, called the location-and-preference joint prediction-based task assignment (LPJTA), is implemented to focus on assigning tasks to workers who are near the task location and willing to perform the task based on predicting locations and preference categories. We theoretically analyze the time complexity and approximation ratio of the proposed methods and construct extensive experiments on three real datasets to empirically verify their effectiveness, comparing with the state-of-the-art baselines.

Fault localization, as a crucial process in network fault management, is the process of deducing the exact source of a failure from a sequence of observed symptoms. Existing methods for this task are either expert system-based or data-driven. However, as communication networks grow and become more complex, conventional expert system-based approaches face problems of inefficiency and inflexibility. Besides, purely data-driven machine learning algorithms are not widely accepted in the industry because of their demand for large training sets and lack of explainability. Inspired by the dual process theory in psychology, we propose a dual-system method, named DualSys, for fault localization in this paper. In the proposed method, a fast data-driven intuitive system and a slow knowledge-driven logical system cooperate sequentially to fulfill the task. To avoid possible conflicts between the two systems, we further propose two conflict-easing mechanisms and incorporate them into the overall process. We validate our method using data from a real-world communication network. Experiment results indicate that our proposed method can get the same accuracy and explainability as knowledge-based approaches and achieve higher efficiency. As a result, we argue that our method provides network operators with a promising choice for efficient fault localization.

We propose a general and scalable global optimization framework directly operating on annotated graph data by introducing a Bayesian graph neural network to approximate the expensive-to-evaluate objectives. It prevents the cubical complexity of Gaussian processes and can scale linearly with the number of observations. Its parallelized variant makes it scalable. We provide strict theoretical support on its convergence. Intensive experiments conducted on both artificial and real-world problems, including molecular discovery and urban road network design, demonstrate the effectiveness of the proposed methods compared with the current state of the art.

Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications. Graph structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool in complex network analysis. However, traditional model-free methods suffer from the expensive computational cost of evaluating graphs; existing vectorial Bayesian optimization methods cannot be directly applied to attributed graphs and have the scalability issue due to the use of Gaussian processes (GPs). To bridge the gap, in this paper, we propose a novel scalable Deep Graph Bayesian Optimization (DGBO) method on attributed graphs. The proposed DGBO prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Intensive experiments are conducted on both artificial and real-world problems, including molecular discovery and urban road network design, and demonstrate the effectiveness of the DGBO compared with the state-of-the-art.

Image recognition aims to identify objects, places, people, or other targeted items in a given image, and has a wide range of social applications such as natural disasters recognition, plant disease detection, and traffic jam detection. Currently state-of-the-art methods of image recognition are based on deep learning and remain a common pattern in designing and using convolutional neural networks (CNNs). However, designing CNNs is extremely time intensive and requires an expert. Neural architecture search (NAS) can solve this problem by automatically identifying architectures of CNNs that are superior to hand-designed ones. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft similarity metrics as input. In this work, we propose a Bayesian graph neural network as a new surrogate, which can automatically extract features from deep neural architectures, and use such learned features to fit and characterize black-box objectives and their uncertainty. Based on the new surrogate, we then develop a graph Bayesian optimization framework to address the challenging task of deep neural architecture search. Experiment results show our method significantly outperforms the comparative methods on benchmark tasks.