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Ziyue Qiao

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21 papers

2 author rows

AAAI Conference 2026 Conference Paper

Evidence-aware Integration and Domain Identification of Spatial Transcriptomics Data

Wei Zhang
Siyu Yi
Lezhi Chen
Yifan Wang
Ziyue Qiao
Yongdao Zhou
Wei Ju

Spatial transcriptomics (ST) enables joint profiling of gene expression and spatial positions, thereby revealing spatially resolved biological functions. However, many existing ST analysis methods often fail to explicitly quantify the belief and uncertainty in decisions caused by noisy ST data, making it difficult to handle spots of varying quality in a fine-grained manner. In addition, domain identification is a fundamental and critical task in ST, but commonly used models that separate expression learning and clustering often struggle to learn cluster-friendly latent representations effectively. To address these issues, we propose PREST, a prototype-based evidence-aware integration framework for ST data. PREST performs multi-scale representation learning with fine-grained attention fusion and introduces learnable class prototypes to quantify belief and uncertainty in model decisions. We aim to align overall belief scores with latent semantic information to enhance uncertainty quantification and prototype learning, thereby promoting the learning of clustering-friendly representations. PREST further integrates an uncertainty-aware reconstruction module and spatial regularization to reduce overfitting to unreliable spots and promote denoised, discriminative representations. Extensive experiments on several benchmark datasets validate the effectiveness and superiority of our proposed PREST across various downstream tasks.

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AAAI Conference 2026 Conference Paper

GROVER: Graph-guided Representation of Omics and Vision with Expert Regulation for Adaptive Spatial Multi-omics Fusion

Yongjun Xiao
Dian Meng
Xinlei Huang
Yanran Liu
Shiwei Ruan
Ziyue Qiao
Xubin Zheng

Effectively modeling multimodal spatial omics data is critical for understanding tissue complexity and underlying biological mechanisms. While spatial transcriptomics, proteomics, and epigenomics capture molecular features, they lack pathological morphological context. Integrating these omics with histopathological images is thus critical for comprehensive disease tissue analysis. However, substantial heterogeneity across omics, imaging, and spatial modalities poses significant challenges. Naive fusion of semantically distinct sources often leads to ambiguous representations. Additionally, the resolution mismatch between high-resolution histology images and lower-resolution sequencing spots complicates spatial alignment. Biological perturbations during sample preparation further distort modality-specific signals, hindering accurate integration. To address these challenges, we propose Graph-guided Representation of Omics and Vision with Expert Regulation for Adaptive Spatial Multi-omics Fusion (GROVER), a novel framework for adaptive integration of spatial multi-omics data. GROVER leverages a Graph Convolutional Network encoder based on Kolmogorov–Arnold Networks to capture the nonlinear dependencies between each modality and its associated spatial structure, thereby producing expressive, modality-specific embeddings. To align these representations, we introduce a spot-feature-pair contrastive learning strategy that explicitly optimizes the correspondence across modalities at each spot. Furthermore, we design a dynamic expert routing mechanism that adaptively selects informative modalities for each spot while suppressing noisy or low-quality inputs. Experiments on real-world spatial omics datasets demonstrate that GROVER outperforms state-of-the-art baselines, providing a robust and reliable solution for multimodal integration.

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AAAI Conference 2025 Conference Paper

Cluster-guided Contrastive Class-imbalanced Graph Classification

Wei Ju
Zhengyang Mao
Siyu Yi
Yifang Qin
Yiyang Gu
Zhiping Xiao
Jianhao Shen
Ziyue Qiao

This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkable success, their modeling ability on imbalanced graph-structured data remains suboptimal, which typically leads to predictions biased towards the majority classes. On the other hand, existing class-imbalanced learning methods in vision may overlook the rich graph semantic substructures of the majority classes and excessively emphasize learning from the minority classes. To address these challenges, we propose a simple yet powerful approach called C3GNN that integrates the idea of clustering into contrastive learning to enhance class-imbalanced graph classification. Technically, C3GNN clusters graphs from each majority class into multiple subclasses, with sizes comparable to the minority class, mitigating class imbalance. It also employs the Mixup technique to generate synthetic samples, enriching the semantic diversity of each subclass. Furthermore, supervised contrastive learning is used to hierarchically learn effective graph representations, enabling the model to thoroughly explore semantic substructures in majority classes while avoiding excessive focus on minority classes. Extensive experiments on real-world graph benchmark datasets verify the superior performance of our proposed method against competitive baselines.

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NeurIPS Conference 2025 Conference Paper

Fourier Clouds: Fast Bias Correction for Imbalanced Semi-Supervised Learning

Jiawei Gu
Yidi Wang
Qingqiang Sun
Xinming Li
Xiao Luo
Ziyue Qiao

Pseudo-label-based Semi-Supervised Learning (SSL) often suffers from classifier bias, particularly under class imbalance, as inaccurate pseudo-labels tend to exacerbate existing biases towards majority classes. Existing methods, such as \textit{CDMAD}\cite{cdmad}, utilize simplistic reference inputs—typically uniform or blank-colored images—to estimate and correct this bias. However, such simplistic references fundamentally ignore realistic statistical information inherent to real datasets, specifically typical color distributions, texture details, and frequency characteristics. This lack of \emph{statistical representativeness} can lead the model to inaccurately estimate its inherent bias, limiting the effectiveness of bias correction, particularly under severe class imbalance or substantial distribution mismatches between labeled and unlabeled datasets. To overcome these limitations, we introduce the \textbf{FARAD} (Fourier-Adapted Reference for Accurate Debiasing) System. This system utilizes random-phase images, constructed by preserving the amplitude spectrum of real data while randomizing the phase spectrum. This strategy ensures two critical properties: (1) \textbf{Semantic Irrelevance}, as randomizing phase removes any structural or recognizable semantic cues, and (2) \textbf{Statistical Representativeness}, as preserving the amplitude spectrum maintains realistic textures, color distributions, and frequency characteristics. Grounded theoretically in classical Fourier analysis, the FARAD System provides a robust, accurate estimation of per-class biases. Furthermore, computational efficiency is enhanced through optimized real-to-complex (R2C) batched Fast Fourier Transforms (FFTs). Comprehensive experiments demonstrate that our approach, significantly improves minority-class accuracy and overall SSL performance, particularly under challenging imbalance scenarios, compared with existing reference-based bias correction methods.

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ICML Conference 2025 Conference Paper

GCAL: Adapting Graph Models to Evolving Domain Shifts

Ziyue Qiao
Qianyi Cai
Hao Dong 0010
Jiawei Gu
Pengyang Wang
Meng Xiao 0001
Xiao Luo 0001
Hui Xiong 0001

This paper addresses the challenge of graph domain adaptation on evolving, multiple out-of-distribution (OOD) graphs. Conventional graph domain adaptation methods are confined to single-step adaptation, making them ineffective in handling continuous domain shifts and prone to catastrophic forgetting. This paper introduces the Graph Continual Adaptive Learning (GCAL) method, designed to enhance model sustainability and adaptability across various graph domains. GCAL employs a bilevel optimization strategy. The "adapt" phase uses an information maximization approach to fine-tune the model with new graph domains while re-adapting past memories to mitigate forgetting. Concurrently, the "generate memory" phase, guided by a theoretical lower bound derived from information bottleneck theory, involves a variational memory graph generation module to condense original graphs into memories. Extensive experimental evaluations demonstrate that GCAL substantially outperforms existing methods in terms of adaptability and knowledge retention.

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AIJ Journal 2025 Journal Article

MATE: Masked optimal transport with dynamic selection for partial label graph learning

Yiyang Gu
Binqi Chen
Zihao Chen
Ziyue Qiao
Xiao Luo
Junyu Luo
Zhiping Xiao
Wei Ju

This paper investigates the problem of partial label graph learning, in which every graph is associated with a set of candidate labels. Previous methods for weakly supervised graph classification often provide pseudo-labels for graph samples that could be overconfident and biased towards the dominant classes, thus resulting in substantial error accumulation. In this paper, we introduce a new framework named Masked Optimal Transport with Dynamic Selection (MATE) for partial label graph learning, which improves the quality of graph assignments from the perspectives of class balancing and uncertainty mining. In particular, our MATE masks probabilities out of candidate sets and then adopts optimal transport to optimize the assignments without class biases. This design is based on the assumption that the true label distribution is class-balanced or nearly balanced, which is common in various training datasets and real-world scenarios. To further reduce potential noise, we propose a novel scoring metric termed partial energy discrepancy (PED) to evaluate the uncertainty of assignments, and then introduce a dynamic selection strategy that modifies the sample-specific thresholds via momentum updating. Finally, these samples are divided into three levels, i. e. , confident, less-confident, and unconfident and each group is trained separately in our collaborative optimization framework. Extensive experiments on various benchmarks demonstrate the superiority of our MATE compared to various state-of-the-art baselines.

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IJCAI Conference 2025 Conference Paper

NeuBM: Mitigating Model Bias in Graph Neural Networks Through Neutral Input Calibration

Jiawei Gu
Ziyue Qiao
Xiao Luo

Graph Neural Networks (GNNs) have shown remarkable performance across various domains, yet they often struggle with model bias, particularly in the presence of class imbalance. This bias can lead to suboptimal performance and unfair predictions, especially for underrepresented classes. We introduce NeuBM (Neutral Bias Mitigation), a novel approach to mitigate model bias in GNNs through neutral input calibration. NeuBM leverages a dynamically updated neutral graph to estimate and correct the inherent biases of the model. By subtracting the logits obtained from the neutral graph from those of the input graph, NeuBM effectively recalibrates the model's predictions, reducing bias across different classes. Our method integrates seamlessly into existing GNN architectures and training procedures, requiring minimal computational overhead. Extensive experiments on multiple benchmark datasets demonstrate that NeuBM significantly improves the balanced accuracy and recall of minority classes, while maintaining strong overall performance. The effectiveness of NeuBM is particularly pronounced in scenarios with severe class imbalance and limited labeled data, where traditional methods often struggle. We provide theoretical insights into how NeuBM achieves bias mitigation, relating it to the concept of representation balancing. Our analysis reveals that NeuBM not only adjusts the final predictions but also influences the learning of balanced feature representations throughout the network.

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IJCAI Conference 2025 Conference Paper

PALA: Class-imbalanced Graph Domain Adaptation via Prototype-anchored Learning and Alignment

Xin Ma
Yifan Wang
Siyu Yi
Wei Ju
Bei Wu
Ziyue Qiao
Chenwei Tang
Jiancheng Lv

Graph domain adaptation is a key subfield of graph transfer learning that aims to bridge domain gaps by transferring knowledge from a label-rich source graph to an unlabeled target graph. However, most existing methods assume balanced labels in the source graph, which often fails in practice and leads to biased knowledge transfer. To address this, in this paper, we propose a prototype-anchored learning and alignment framework for class-imbalanced graph domain adaptation. Specifically, we incorporate pointwise node mutual information into the graph encoder to capture high-order topological proximity and learn generalized node representations. Leveraging this, we then introduce categorical prototypes with adversarial proto-instances for prototype-anchored learning and recalibration to represent the source graph under an imbalanced class distribution. Finally, we introduce a weighted prototype contrastive adaptation strategy that aligns target pseudo-labels with source prototypes to handle class imbalance during adaptation. Extensive experiments show that our PALA outperforms the state-of-the-art methods. Our code is available at https: //github. com/maxin88scu/PALA.

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AAAI Conference 2025 Conference Paper

PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis

Xinlei Huang
Zhiqi Ma
Dian Meng
Yanran Liu
Shiwei Ruan
Qingqiang Sun
Xubin Zheng
Ziyue Qiao

Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of semantic information. In addition, the common lack of spot annotation and class number priors in practice further hinders the optimization of spatial multi-modal omics models. Here, we propose a novel spatial multi-modal omics resolved framework, termed Prototype-aware Graph Adaptative Aggregation for Spatial Multi-modal Omics Analysis (PRAGA). PRAGA constructs a dynamic graph to capture latent semantic relations and comprehensively integrate spatial information and feature semantics. The learnable graph structure can also denoise perturbations by learning cross-modal knowledge. Moreover, a dynamic prototype contrastive learning is proposed based on the dynamic adaptability of Bayesian Gaussian Mixture Models to optimize the multi-modal omics representations for unknown biological priors. Quantitative and qualitative experiments on simulated and real datasets with 7 competing methods demonstrate the superior performance of PRAGA.

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NeurIPS Conference 2025 Conference Paper

Refining Norms: A Post-hoc Framework for OOD Detection in Graph Neural Networks

Jiawei Gu
Ziyue Qiao
Zechao Li

Graph Neural Networks (GNNs) are increasingly deployed in mission-critical tasks, yet they often encounter inputs that lie outside their training distribution, leading to unreliable or overconfident predictions. To address this limitation, we present RAGNOR (Robust Aggregation Graph Norm for Outlier Recognition), a post-hoc approach that leverages embedding norms for robust out-of-distribution (OOD) detection on both node-level and graph-level tasks. Unlike previous methods designed primarily for image domains, RAGNOR directly tackles the relational challenges intrinsic to graphs: local contamination by anomalous neighbors, disparate norm scales across classes or roles, and insufficient references for boundary or low-degree nodes. By combining global Z-score normalization, median-based local aggregation, and multi-hop blending, RAGNOR effectively refines raw norm signals into robust OOD scores while incurring minimal overhead and requiring no retraining of the original GNN. Experimental evaluations on multiple benchmarks demonstrate that RAGNOR not only achieves competitive or superior detection performance compared to alternative techniques, but also provides an intuitive, modular design that can be readily integrated into existing graph pipelines.

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IJCAI Conference 2025 Conference Paper

Rethinking Graph Contrastive Learning Through Relative Similarity Preservation

Zhiyuan Ning
Pengfei Wang
Ziyue Qiao
Pengyang Wang
Yuanchun Zhou

Graph contrastive learning (GCL) has achieved remarkable success by following the computer vision paradigm of preserving absolute similarity between augmented views. However, this approach faces fundamental challenges in graphs due to their discrete, non-Euclidean nature -- view generation often breaks semantic validity and similarity verification becomes unreliable. Through analyzing 11 real-world graphs, we discover a universal pattern transcending the homophily-heterophily dichotomy: label consistency systematically diminishes as structural distance increases, manifesting as smooth decay in homophily graphs and oscillatory decay in heterophily graphs. We establish theoretical guarantees for this pattern through random walk theory, proving label distribution convergence and characterizing the mechanisms behind different decay behaviors. This discovery reveals that graphs naturally encode relative similarity patterns, where structurally closer nodes exhibit collectively stronger semantic relationships. Leveraging this insight, we propose RELGCL, a novel GCL framework with complementary pairwise and listwise implementations that preserve these inherent patterns through collective similarity objectives. Extensive experiments demonstrate that our method consistently outperforms 20 existing approaches across both homophily and heterophily graphs, validating the effectiveness of leveraging natural relative similarity over artificial absolute similarity.

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ICML Conference 2025 Conference Paper

Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis

Weiyu Guo
Ziyue Qiao
Ying Sun 0006
Yijie Xu
Hui Xiong 0001

Gesture recognition based on surface electromyography (sEMG) has been gaining importance in many 3D Interactive Scenes. However, sEMG is easily influenced by various forms of noise in real-world environments, leading to challenges in providing long-term stable interactions through sEMG. Existing methods often struggle to enhance model noise resilience through various predefined data augmentation techniques. In this work, we revisit the problem from a short-term enhancement perspective to improve precision and robustness against various common noisy scenarios with learnable denoise using sEMG intrinsic pattern information and sliding-window attention. We propose a Short Term Enhancement Module(STEM), which can be easily integrated with various models. STEM offers several benefits: 1) Noise-resistant, enhanced robustness against noise without manual data augmentation; 2) Adaptability, adaptable to various models; and 3) Inference efficiency, achieving short-term enhancement through minimal weight-sharing in an efficient attention mechanism. In particular, we incorporate STEM into a transformer, creating the Short-Term Enhanced Transformer (STET). Compared with best-competing approaches, the impact of noise on STET is reduced by more than 20%. We report promising results on classification and regression tasks and demonstrate that STEM generalizes across different gesture recognition tasks. The code is available at https: //anonymous. 4open. science/r/short_term_semg.

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NeurIPS Conference 2025 Conference Paper

Revitalizing SVD for Global Covariance Pooling: Halley’s Method to Overcome Over-Flattening

Jiawei Gu
Ziyue Qiao
Xinming Li
Zechao Li

Global Covariance Pooling (GCP) has garnered increasing attention in visual recognition tasks, where second-order statistics frequently yield stronger representations than first-order approaches. However, two main streams of GCP---Newton--Schulz-based iSQRT-COV and exact or near-exact SVD methods---struggle at opposite ends of the training spectrum. While iSQRT-COV stabilizes early learning by avoiding large gradient explosions, it over-compresses significant eigenvalues in later stages, causing an \emph{over-flattening} phenomenon that stalls final accuracy. In contrast, SVD-based methods excel at preserving the high-eigenvalue structure essential for deep networks but suffer from sensitivity to small eigenvalue gaps early on. We propose \textbf{Halley-SVD}, a high-order iterative method that unites the smooth gradient advantages of iSQRT-COV with the late-stage fidelity of SVD. Grounded in Halley's iteration, our approach obviates explicit divisions by $(\lambda_i - \lambda_j)$ and forgoes threshold- or polynomial-based heuristics. As a result, it prevents both early gradient explosions and the excessive compression of large eigenvalues. Extensive experiments on CNNs and transformer architectures show that Halley-SVD consistently and robustly outperforms iSQRT-COV at large model scales and batch sizes, achieving higher overall accuracy without mid-training switches or custom truncations. This work provides a new solution to the long-standing dichotomy in GCP, illustrating how high-order methods can balance robustness and spectral precision to fully harness the representational power of modern deep networks.

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AAAI Conference 2025 Conference Paper

Single-View Graph Contrastive Learning with Soft Neighborhood Awareness

Qingqiang Sun
Chaoqi Chen
Ziyue Qiao
Xubin Zheng
Kai Wang

Most graph contrastive learning (GCL) methods heavily rely on cross-view contrast, thus facing several concomitant challenges, such as the complexity of designing effective augmentations, the potential for information loss between views, and increased computational costs. To mitigate reliance on cross-view contrasts, we propose SIGNA, a novel single-view graph contrastive learning framework. Regarding the inconsistency between structural connection and semantic similarity of neighborhoods, we resort to soft neighborhood awareness for GCL. Specifically, we leverage dropout to obtain structurally-related yet randomly-noised embedding pairs for neighbors, which serve as potential positive samples. At each epoch, the role of partial neighbors is switched from positive to negative, leading to probabilistic neighborhood contrastive learning effect. Moreover, we propose a normalized Jensen-Shannon divergence estimator for a better effect of contrastive learning. Experiments on diverse node-level tasks demonstrate that our simple single-view GCL framework consistently outperforms existing methods by margins of up to 21.74% (PPI). In particular, with soft neighborhood awareness, SIGNA can adopt MLPs instead of complicated GCNs as the encoder in transductive learning tasks, thus speeding up its inference process by 109× to 331×.

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IJCAI Conference 2025 Conference Paper

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps

Jiawei Gu
Ziyue Qiao
Zechao Li

The task of graph-level out-of-distribution (OOD) detection is crucial for deploying graph neural networks in real-world settings. In this paper, we observe a significant difference in the relationship between the largest and second-largest eigenvalues of the Laplacian matrix for in-distribution (ID) and OOD graph samples: OOD samples often exhibit anomalous spectral gaps (the difference between the largest and second-largest eigenvalues). This observation motivates us to propose SpecGap, an effective post-hoc approach for OOD detection on graphs. SpecGap adjusts features by subtracting the component associated with the second-largest eigenvalue, scaled by the spectral gap, from the high-level features (i. e. , X - (λn - λn-1) u_n-1 v_n-1^T). SpecGap achieves state-of-the-art performance across multiple benchmark datasets. We present extensive ablation studies and comprehensive theoretical analyses to support our empirical results. As a parameter-free post-hoc method, SpecGap can be easily integrated into existing graph neural network models without requiring any additional training or model modification.

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ICLR Conference 2025 Conference Paper

Towards Continuous Reuse of Graph Models via Holistic Memory Diversification

Ziyue Qiao
Junren Xiao
Qingqiang Sun
Meng Xiao 0001
Xiao Luo 0001
Hui Xiong 0001

This paper addresses the challenge of incremental learning in growing graphs with increasingly complex tasks. The goal is to continuously train a graph model to handle new tasks while retaining proficiency in previous tasks via memory replay. Existing methods usually overlook the importance of memory diversity, limiting in selecting high-quality memory from previous tasks and remembering broad previous knowledge within the scarce memory on graphs. To address that, we introduce a novel holistic Diversified Memory Selection and Generation (DMSG) framework for incremental learning in graphs, which first introduces a buffer selection strategy that considers both intra-class and inter-class diversities, employing an efficient greedy algorithm for sampling representative training nodes from graphs into memory buffers after learning each new task. Then, to adequately rememorize the knowledge preserved in the memory buffer when learning new tasks, a diversified memory generation replay method is introduced. This method utilizes a variational layer to generate the distribution of buffer node embeddings and sample synthesized ones for replaying. Furthermore, an adversarial variational embedding learning method and a reconstruction-based decoder are proposed to maintain the integrity and consolidate the generalization of the synthesized node embeddings, respectively. Extensive experimental results on publicly accessible datasets demonstrate the superiority of DMSG over state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning

Haomiao Qiu
Miao Zhang
Ziyue Qiao
Liqiang Nie

Continual Learning (CL) aims to enable models to continuously acquire new knowledge from a sequence of tasks with avoiding the forgetting of learned information. However, existing CL methods only rely on the parameters of the most recent task for inference, which makes them susceptible to catastrophic forgetting. Inspired by the recent success of model merging techniques, we propose Perturb-and-Merge (P&M), a novel continual learning framework that integrates model merging into the CL paradigm to mitigate forgetting. Specifically, after training on each task, P&M constructs a new model by forming a convex combination of the previous model and the newly trained task-specific model. Through theoretical analysis, We minimize the total loss increase across all tasks and derive a closed-form solution for the merging coefficient under mild assumptions. To further improve the performance of the merged model, we observe that the degradation introduced during merging can be alleviated by a regularization term composed of the task vector and the Hessian matrix of the loss function. Interestingly, we show that this term can be efficiently approximated using second-order symmetric finite differences, and a stochastic perturbation strategy along the task vector direction is accordingly devised which incurs no additional forward or backward passes while providing an effective approximation of the regularization term. Finally, we combine P&M with LoRA, a parameter-efficient fine-tuning method, to reduce memory overhead. Our proposed approach achieves state-of-the-art performance on several continual learning benchmark datasets. The code is available at \url{https: //github. com/qhmiao/P-M-for-Continual-Learning}.

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NeurIPS Conference 2024 Conference Paper

SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network

Weiyu Guo
Ying Sun
Yijie Xu
Ziyue Qiao
Yongkui Yang
Hui Xiong

Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) High Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs' ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture's performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines ($89. 26\%$). Moreover, the actual deployment on the CPU demonstrated a latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture's potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https: //github. com/guoweiyu/SpGesture/.

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IJCAI Conference 2023 Conference Paper

Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning

Hao Dong
Zhiyuan Ning
Pengyang Wang
Ziyue Qiao
Pengfei Wang
Yuanchun Zhou
Yanjie Fu

Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of works have been made to model the historical structural and temporal characteristics for the reasoning task. Most existing works model the graph structure mainly depending on entity representation. However, the magnitude of TKG entities in real-world scenarios is considerable, and an increasing number of new entities will arise as time goes on. Therefore, we propose a novel architecture modeling with relation feature of TKG, namely aDAptivE path-MemOry Network (DaeMon), which adaptively models the temporal path information between query subject and each object candidate across history time. It models the historical information without depending on entity representation. Specifically, DaeMon uses path memory to record the temporal path information derived from path aggregation unit across timeline considering the memory passing strategy between adjacent timestamps. Extensive experiments conducted on four real-world TKG datasets demonstrate that our proposed model obtains substantial performance improvement and outperforms the state-of-the-art up to 4. 8% absolute in MRR.

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IJCAI Conference 2023 Conference Paper

Semi-supervised Domain Adaptation in Graph Transfer Learning

Ziyue Qiao
Xiao Luo
Meng Xiao
Hao Dong
Yuanchun Zhou
Hui Xiong

As a specific case of graph transfer learning, unsupervised domain adaptation on graphs aims for knowledge transfer from label-rich source graphs to unlabeled target graphs. However, graphs with topology and attributes usually have considerable cross-domain disparity and there are numerous real-world scenarios where merely a subset of nodes are labeled in the source graph. This imposes critical challenges on graph transfer learning due to serious domain shifts and label scarcity. To address these challenges, we propose a method named Semi-supervised Graph Domain Adaptation (SGDA). To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding. Thus, the node classifier trained on labeled source nodes can be transferred to the target nodes. Moreover, to address the label scarcity, we propose pseudo-labeling on unlabeled nodes, which improves classification on the target graph via measuring the posterior influence of nodes based on their relative position to the class centroids. Finally, extensive experiments on a range of publicly accessible datasets validate the effectiveness of our proposed SGDA in different experimental settings.

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AAAI Conference 2021 Short Paper

Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract)

Ziyue Qiao
Zhiyuan Ning
Yi Du
Yuanchun Zhou

Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the contextual information of entity and relation. Here, we extract contexts of entities and relations from the triplets which they compose. We propose a model named AggrE, which conducts efficient aggregations respectively on entity context and relation context in multihops, and learns context-enhanced entity and relation embeddings for knowledge graph completion. The experiment results show that AggrE is competitive to existing models.

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