Landslide susceptibility mapping (LSM) is crucial for disaster prevention and regional planning; however, existing approaches face challenges in large-scale and heterogeneous environments owing to high computational costs and limited capacity for modeling long-range dependencies. To overcome these limitations, we proposed a scalable Graph Transformer with a dual-branch design. It integrates a linear attention-based Transformer, which reduces the computational complexity for global dependency modeling, and a Scalable Inception Graph Neural Network (SIGN), which emphasizes local feature extraction and multiscale feature integration. Additionally, consistency regularization loss was incorporated to enhance the robustness and generalizability. Experiments in the Upper Yellow River Basin, which is characterized by complex and diverse geomorphology, show that the proposed model achieves 81.7% accuracy and 88.8% recall, outperforming traditional machine learning methods and mainstream Graph Neural Networks (GNNs) with improvements of 5% and 3.7%, respectively. Furthermore, we introduced the Mean Average Distance (MAD) and the gap of MAD values (MADGap) to quantify the over-smoothing problem in a GNN-based LSM. The proposed model exhibited consistently higher MADGap values across multiple subgraphs than mainstream GNNs, indicating its superior capacity in mitigating over-smoothing and confirming its overall accuracy and effectiveness for large-scale LSM.

Iceberg orders, which involve splitting large financial orders into smaller batches to conceal trading intent, pose significant challenges to market stability. To address this challenge, we propose a Multi-Hierarchical Hidden Markov Model (MH-HMM) that captures multi-scale stochastic states in trading sequences. The model employs a layered stacking mechanism to enhance its ability to detect hidden order patterns and incorporates an attention mechanism for adaptive temporal input selection. Comprehensive experiments on three realistic financial datasets and twenty multivariate time series classification datasets demonstrate that the proposed MH-HMM outperforms the state-of-the-art benchmarks on most datasets. Effectiveness experiments demonstrate that our approach effectively identifies iceberg orders and improves market trend prediction, providing a valuable tool for financial market analysis and algorithmic trading strategies. Code is available at https://github.com/tenyee-space/FinRL_MHHMM/tree/main.

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning. However, EntMin emphasizes prediction discriminability while neglecting prediction diversity. To alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled samples. By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding. Inspired by this finding, we propose a Label-Encoding Risk Minimization (LERM). It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label encodings. As a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a plugin. Theoretically, we analyze the relationships between LERM and ERM as well as EntMin. Empirically, we verify the superiority of the LERM under several label insufficient scenarios. The codes are available at https://github.com/zhangyl660/LERM.

Large-scale diffusion models are adept at generating high-fidelity images and facilitating image editing and interpolation. However, they have limitations when tasked with generating images in dynamic, evolving domains. In this paper, we introduce Terra, a novel Time-varying low-rank adapter that offers a fine-tuning framework specifically tailored for domain flow generation. The key innovation of Terra lies in its construction of a continuous parameter manifold through a time variable, with its expressive power analyzed theoretically. This framework not only enables interpolation of image content and style but also offers a generation-based approach to address the domain shift problems in unsupervised domain adaptation and domain generalization. Specifically, Terra transforms images from the source domain to the target domain and generates interpolated domains with various styles to bridge the gap between domains and enhance the model generalization, respectively. We conduct extensive experiments on various benchmark datasets, empirically demonstrate the effectiveness of Terra. Our source code is publicly available on https://github.com/zwebzone/terra.

Network data in real-world is error-prone, which results in inaccurate results when performing network analysis or modeling such as node classification and link prediction on these flawed networks. In this paper, we target at reconstructing a reliable network from a flawed network, named as network enhancement. Specifically, network enhancement aims to both detect the noisy links which are observed in the network but should not exist in the real world, and predict the missing links that indeed exist in the real world yet being unobserved in the network. Different from existing works that calculate a unified score to measure the above two kinds of links, we propose E-Net, an end-to-end graph neural network model, to leverage the mutual influence of the two tasks to achieve both the goals more effectively. Because on one hand, detecting noisy links can benefit the performance of predicting missing links; and on the other hand, predicting missing links can provide indirect supervision for detecting noisy links when the labels of the noisy links are unavailable. The experimental results on several datasets show that the proposed model obtains significant improvement for predicting missing links and detecting noisy links.

Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queries to it. These assumptions are, however, unrealistic. To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries. To design such an attack strategy, we first propose a generic graph filter to unify different families of graph-based models. The strength of attacks can then be quantified by the change in the graph filter before and after attack. By maximizing this change, we are able to find an effective attack strategy, regardless of the underlying model. To solve this optimization problem, we also propose a relaxation technique and approximation theories to reduce the difficulty as well as the computational expense. Experiments demonstrate that, even with no exposure to the model, the Macro-F1 drops 6.4% in node classification and 29.5% in graph classification, which is a significant result compared with existent works.

Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they are shown to be able to generalize to various downstream applications. Yet, the adversarial robustness of such pre-trained graph learning models remains largely unexplored. More importantly, most existing defense techniques designed for end-to-end graph representation learning methods require pre-specified label definitions, and thus cannot be directly applied to the pre-training methods. In this paper, we propose an unsupervised defense technique to robustify pre-trained deep graph models, so that the perturbations on the input graph can be successfully identified and blocked before the model is applied to different downstream tasks. Specifically, we introduce a mutual information-based measure, graph representation vulnerability (GRV), to quantify the robustness of graph encoders on the representation space. We then formulate an optimization problem to learn the graph representation by carefully balancing the trade-off between the expressive power and the robustness (i.e., GRV) of the graph encoder. The discrete nature of graph topology and the joint space of graph data make the optimization problem intractable to solve. To handle the above difficulty and to reduce computational expense, we further relax the problem and thus provide an approximate solution. Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks. Extensive experiments demonstrate that even without access to labels and tasks, our model is still able to enhance robustness against adversarial attacks on three downstream tasks (node classification, link prediction, and community detection) by an average of +16.5% compared with existing methods.

Network data in real-world tends to be error-prone. In this paper, we aim to reconstruct a reliable network from a fiawed, undirected, unweighted network, a process referred to network enhancement. More specifically, network enhancement aims to detect the noisy links that are observed in the network but should not exist in the real world, as well as to predict the missing links that do indeed exist in the real world yet remain unobserved. While some attempts have been made to detect either noisy links or missing links, few of these works have considered unifying these two tasks, even though they are inter-dependent and capable of mutually boosting each others’ performance. In this paper, we therefore propose E-Net, an end-toend graph neural network model, to leverage the mutual influence of these two tasks in order to achieve both goals more effectively. On one hand, detecting noisy links can benefit the performance of missing link prediction, while on the other hand, predicting missing links can provide indirect supervision for detecting noisy link detection when the labels of these noisy links are unavailable. The experimental results demonstrate the significance of our proposed model in missing link prediction and noisy link detection task.

In order to solve the problem of long diagnosis time for fault diagnosis algorithms in large-scale environments, this paper proposes a service fault location algorithm based on network characteristics. First, based on the virtual network mapping data, the service is associated with the underlying resources to build a Bayesian fault location model. Second, by analyzing the network topology and the running status of each service, the probability of network node failure is calculated based on the multi-attribute characteristics. Finally, the fault location is achieved by calculating the set of suspected faults with the strongest ability to explain abnormal symptoms. The experimental part compares the algorithm of this paper with the classical algorithm, and verifies that the algorithm of this paper improves the accuracy of fault diagnosis and reduces the time cost of fault diagnosis.

In order to solve the problem of low accuracy of fault diagnosis algorithms caused by increased network dynamics, this paper proposes a dynamic network fault diagnosis algorithm under 5G network slices. By calculating the failure credibility of each link, a clustering algorithm is used to cluster the symptoms to correct the false symptoms. When constructing the fault propagation model, the time slice t is added to the fault propagation model, thereby depicting different network models in different time slices. Finally, locate the fault by solving the original state and current state of the faulty node. The experimental part compares the algorithm of this paper with the existing algorithm, and verifies that the algorithm of this paper improves the performance of the fault diagnosis algorithm.

Game bots are automated programs that assist cheating users, leading to an imbalance in the game ecosystem and the collapse of user interest. Online games provide immersive gaming experience and attract many loyal fans. However, game bots have proliferated in volume and method, evolving with the real-world detection methods and showing strong diversity, leaving game bot detection efforts extremely difficult. Existing game bot detection techniques mostly rely on handcrafted features or time-series based features instead of fully utilizing player behavior sequences. In this regard, a more reasonable way should be learning user patterns from player behavior sequences when facing the fast-changing nature of game bots. Here we propose a general game bot detection framework for massively multiplayer online role playing games termed NGUARD+ (denoting NetEase Games’ Guard), which captures user patterns in order to identify game bots from player behavior sequences. NGUARD+ mainly employs attention-based methods to automatically differentiate game bots from humans. We provide a combination of supervised and unsupervised methods for game bot detection to detect game bots and new type of game bots even when the labels of game bots are limited. Specifically, we propose the following two variants for attention-based sequence modeling: Attention based Bidirectional Long Short-Term Memory Networks (ABLSTM) and Hierarchical Self-Attention Network (HSAN) as our supervised models. ABLSTM is keen on inducing certain inductive biases which makes learning more reasonable as well as capturing local dependency and global information, while HSAN could handle much longer behavior sequences with less memory and higher computational efficiency. Experiments conducted on a real-world dataset show that NGUARD+ can achieve remarkable performance improvement compared to traditional methods. Moreover, NGUARD+ can reveal outstanding robustness for game bots in mutated patterns and even in completely unseen patterns.