Query：
学者姓名：傅仰耿
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
Abstract ：
针对异构图神经网络模型依赖元路径和复杂聚合操作导致元路径受限与高成本的不足,提出一种基于注意力融合机制和拓扑关系挖掘的异构图神经网络模型(FTHGNN).该模型首先使用一种轻量级的注意力融合机制,融合全局关系信息和局部节点信息,以较低的时空开销实现更有效的消息聚合;接着使用一种无需先验知识的拓扑关系挖掘方法替代元路径方法,挖掘图上的高阶邻居关系,并引入对比学习捕获图上的高阶语义信息;最后,在4个广泛使用的现实世界异构图数据集上进行的充分实验,验证了 FTHGNN简单而高效,在分类预测准确率上超越了绝大多数现有模型.
Keyword ：
图神经网络 图神经网络 对比学习 对比学习 异构图 异构图 注意力机制 注意力机制
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 陈金杰 , 王一蕾 , 傅仰耿 . 注意力融合机制和拓扑关系挖掘的异构图神经网络 [J]. | 福州大学学报（自然科学版） , 2025 , 53 (1) : 1-9 . |
| MLA | 陈金杰 等. "注意力融合机制和拓扑关系挖掘的异构图神经网络" . | 福州大学学报（自然科学版） 53 . 1 (2025) : 1-9 . |
| APA | 陈金杰 , 王一蕾 , 傅仰耿 . 注意力融合机制和拓扑关系挖掘的异构图神经网络 . | 福州大学学报（自然科学版） , 2025 , 53 (1) , 1-9 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The Belief Rule -Based (BRB) system faces the rule combination explosion issue, making it challenging to construct the rule base efficiently. The Extended Belief Rule -Based (EBRB) system offers a solution to this problem by using data -driven methods. However, using EBRB system requires the traversal of the entire rule base, which can be time-consuming and result in the activation of many irrelevant rules, leading to an incorrect decision. Existing search optimization methods can somewhat solve this issue, but they have limitations. Moreover, the calculation of the rule activation weight only considers the similarity between input data and a single rule, ignoring the influence of the rule linkage. To address these problems, we propose a new EBRB system based on the K -Nearest Neighbor graph index (Graph-EBRB). We introduce the Hierarchical Navigable Small World (HNSW) algorithm to create the K -Nearest Neighbor graph index of the EBRB system. This index allows us to efficiently search and activate a set of key rules. We also propose a new activation weight calculation method based on the Graph Convolution Neural Network (GCN), and we optimize the system performance using a parameter learning strategy. We conduct a comprehensive experiment on 14 commonly used public data sets, and the results show that Graph-EBRB system significantly improves the reasoning efficiency and accuracy of the EBRB system. Finally, we apply the Graph-EBRB system to tree disease identification and achieve excellent classification performance, identifying over 90% of the diseased trees on the complete dataset.
Keyword ：
Extended belief rule-based system Extended belief rule-based system Graph convolution neural network Graph convolution neural network Hierarchical navigable small world graph Hierarchical navigable small world graph K-Nearest Neighbor graph K-Nearest Neighbor graph
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Fu, Yang-Geng , Lin, Xin-Yi , Fang, Geng-Chao et al. A novel extended rule-based system based on K-Nearest Neighbor graph [J]. | INFORMATION SCIENCES , 2024 , 662 . |
| MLA | Fu, Yang-Geng et al. "A novel extended rule-based system based on K-Nearest Neighbor graph" . | INFORMATION SCIENCES 662 (2024) . |
| APA | Fu, Yang-Geng , Lin, Xin-Yi , Fang, Geng-Chao , Li, Jin , Cai, Hong-Yi , Gong, Xiao-Ting et al. A novel extended rule-based system based on K-Nearest Neighbor graph . | INFORMATION SCIENCES , 2024 , 662 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Anomaly detection in time series data is crucial for many fields such as healthcare, meteorology, and industrial fault detection. However, traditional unsupervised time series anomaly detection methods suffer from biased anomaly measurement under contaminated training data. Most of existing methods employ hard strategies for contamination calibration by assigning pseudo -label to training data. These hard strategies rely on threshold selection and result in suboptimal performance. To address this problem, in this paper, we propose a novel unsupervised anomaly detection framework for contaminated time series (NegCo), which builds an effective soft contamination calibration strategy by exploiting the observed negative correlation between semantic representation and anomaly detection inherent within the autoencoder framework. We innovatively redefine anomaly detection in data contamination scenarios as an optimization problem rooted in this negative correlation. To model this negative correlation, we introduce a dual construct: morphological similarity captures semantic distinctions relevant to normality, while reconstruction consistency quantifies deviations indicative of anomalies. Firstly, the morphological similarity is effectively measured based on the representative normal samples generated from the center of the learned Gaussian distribution. Then, an anomaly measurement calibration loss function is designed based on negative correlation between morphological similarity and reconstruction consistency, to calibrate the biased anomaly measurement caused by contaminated samples. Extensive experiments on various time series datasets show that the proposed NegCo outperforms stateof-the-art baselines, achieving an improvement of 6.2% to 26.8% in Area Under the Receiver Operating Characteristics (AUROC) scores, particularly in scenarios with heavily contaminated training data.
Keyword ：
Anomaly detection Anomaly detection Data contamination Data contamination Negative correlation Negative correlation Time series Time series
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Xiaohui , Li, Zuoyong , Fan, Haoyi et al. Exploiting negative correlation for unsupervised anomaly detection in contaminated time series [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 249 . |
| MLA | Lin, Xiaohui et al. "Exploiting negative correlation for unsupervised anomaly detection in contaminated time series" . | EXPERT SYSTEMS WITH APPLICATIONS 249 (2024) . |
| APA | Lin, Xiaohui , Li, Zuoyong , Fan, Haoyi , Fu, Yanggeng , Chen, Xinwei . Exploiting negative correlation for unsupervised anomaly detection in contaminated time series . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 249 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
提出一种基于曲率图卷积的非均匀分组与掩码策略，用以优化掩码自编码器.首先，提出曲率图卷积以避免固定邻域导致的归纳偏差；其次，在曲率图卷积后引入图池化层，根据点云局部特征进行池化操作并分组；最后，在池化层输出特征的基础上学习每个分组的掩码概率来避免冗余.实验结果表明，本方法能有效提高点云掩码自编码器在下游任务的泛化效果，在ModelNet40上的分类精度达到93.7%,在Completion3Dv2上的补全精度达到5.08,均优于目前主流方法.
Keyword ：
图卷积神经网络 图卷积神经网络 点云 点云 自监督学习 自监督学习 自编码器 自编码器 预训练 预训练
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 黄敏明 , 傅仰耿 . 基于曲率图卷积的非均匀点云掩码自编码器 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (01) : 1-6 . |
| MLA | 黄敏明 et al. "基于曲率图卷积的非均匀点云掩码自编码器" . | 福州大学学报(自然科学版) 52 . 01 (2024) : 1-6 . |
| APA | 黄敏明 , 傅仰耿 . 基于曲率图卷积的非均匀点云掩码自编码器 . | 福州大学学报(自然科学版) , 2024 , 52 (01) , 1-6 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Graph neural networks (GNNs) have achieved excellent performances in many graph-related tasks. However, they need appropriate pooling operations to deal with the graph classification tasks, and thus, they may suffer from some limitations such as information loss and ignorance of the part-whole relationships. CapsGNN is proposed to solve the above-mentioned issues, but suffers from high time and space complexities leading to its poor scalability. In this paper, we propose a novel, effective and efficient graph capsule network called LightCapsGNN. First, we devise a fast voting mechanism (called LightVoting) implemented via linear combinations of K shared transformation matrices to reduce the number of trainable parameters in the voting procedure. Second, an improved reconstruction layer is proposed to encourage our model to capture more informative and essential knowledge of the input graph. Third, other improvements are combined to further accelerate our model, e.g., matrix capsules and a trainable routing mechanism. Finally, extensive experiments are conducted on the popular real-world graph benchmarks in the graph classification tasks and the proposed model can achieve competitive or even better performance compared to ten baselines or state-of-the-art models. Furthermore, compared to other CapsGNNs, the proposed model reduce almost 99% learnable parameters and 31.1% running time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Keyword ：
Capsule networks Capsule networks Graph neural networks Graph neural networks Routing Routing
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yan, Y. , Li, J. , Xu, S. et al. LightCapsGNN: light capsule graph neural network for graph classification [J]. | Knowledge and Information Systems , 2024 , 66 (10) : 6363-6386 . |
| MLA | Yan, Y. et al. "LightCapsGNN: light capsule graph neural network for graph classification" . | Knowledge and Information Systems 66 . 10 (2024) : 6363-6386 . |
| APA | Yan, Y. , Li, J. , Xu, S. , Chen, X. , Liu, G. , Fu, Y.-G. . LightCapsGNN: light capsule graph neural network for graph classification . | Knowledge and Information Systems , 2024 , 66 (10) , 6363-6386 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Graph Neural Networks (GNNs) have demonstrated great potential in achieving outstanding performance in various graph -related tasks, e.g., graph classification and link prediction. However, most of them suffer from the following issue: shallow networks capture very limited knowledge. Prior works design deep GNNs with more layers to solve the issue, which however introduces a new challenge, i.e., the infamous oversmoothness. Graph representation over emphasizes node features but only considers the static graph structure with a uniform weight are the key reasons for the over -smoothness issue. To alleviate the issue, this paper proposes a Dynamic Weighting Strategy (DWS) for addressing over -smoothness. We first employ Fuzzy CMeans (FCM) to cluster all nodes into several groups and get each node's fuzzy assignment, based on which a novel metric function is devised for dynamically adjusting the aggregation weights. This dynamic weighting strategy not only enables the intra-cluster interactions, but also inter -cluster aggregations, which well addresses undifferentiated aggregation caused by uniform weights. Based on DWS, we further design a Structure Augmentation (SA) step for addressing the issue of underutilizing the graph structure, where some potentially meaningful connections (i.e., edges) are added to the original graph structure via a parallelable KNN algorithm. In general, the optimized Dynamic Weighting Strategy with Structure Augmentation (DWSSA) alleviates over -smoothness by reducing noisy aggregations and utilizing topological knowledge. Extensive experiments on eleven homophilous or heterophilous graph benchmarks demonstrate the effectiveness of our proposed method DWSSA in alleviating over -smoothness and enhancing deep GNNs performance.
Keyword ：
Clustering Clustering Deep graph neural networks Deep graph neural networks Node classification Node classification Over-smoothness Over-smoothness Structure augmentation Structure augmentation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Qirong , Li, Jin , Ye, Qingqing et al. DWSSA: Alleviating over-smoothness for deep Graph Neural Networks [J]. | NEURAL NETWORKS , 2024 , 174 . |
| MLA | Zhang, Qirong et al. "DWSSA: Alleviating over-smoothness for deep Graph Neural Networks" . | NEURAL NETWORKS 174 (2024) . |
| APA | Zhang, Qirong , Li, Jin , Ye, Qingqing , Lin, Yuxi , Chen, Xinlong , Fu, Yang-Geng . DWSSA: Alleviating over-smoothness for deep Graph Neural Networks . | NEURAL NETWORKS , 2024 , 174 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Recent studies have shown that Graph Transformers (GTs) can be effective for specific graph-level tasks. However, when it comes to node classification, training GTs remains challenging, especially in semi-supervised settings with a severe scarcity of labeled data. Our paper aims to address this research gap by focusing on semi-supervised node classification. To accomplish this, we develop a curriculum-enhanced attention distillation method that involves utilizing a Local GT teacher and a Global GT student. Additionally, we introduce the concepts of in-class and out-of-class and then propose two improvements, out-of-class entropy and top-k pruning, to facilitate the student's out-of-class exploration under the teacher's in-class guidance. Taking inspiration from human learning, our method involves a curriculum mechanism for distillation that initially provides strict guidance to the student and gradually allows for more out-of-class exploration by a dynamic balance. Extensive experiments show that our method outperforms many state-of-the-art methods on seven public graph benchmarks, proving its effectiveness. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Huang, Y. , Li, J. , Chen, X. et al. TRAINING GRAPH TRANSFORMERS VIA CURRICULUM-ENHANCED ATTENTION DISTILLATION [未知]. |
| MLA | Huang, Y. et al. "TRAINING GRAPH TRANSFORMERS VIA CURRICULUM-ENHANCED ATTENTION DISTILLATION" [未知]. |
| APA | Huang, Y. , Li, J. , Chen, X. , Fu, Y.-G. . TRAINING GRAPH TRANSFORMERS VIA CURRICULUM-ENHANCED ATTENTION DISTILLATION [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With increasing popularity and larger real-world applicability, graph self-supervised learning (GSSL) can significantly reduce labeling costs by extracting implicit input supervision. As a promising example, graph masked auto-encoders (GMAE) can encode rich node knowledge by recovering the masked input components, e.g., features or edges. Despite their competitiveness, existing GMAEs focus only on neighboring information reconstruction, which totally ignores distant multi-hop semantics and thus fails to capture global knowledge. Furthermore, many GMAEs cannot scale on large-scale graphs since they suffer from memory bottlenecks with unavoidable full-batch training. To address these challenges and facilitate “high-level” discriminative semantics, we propose a simple yet effective framework (i.e., HopMAE) to encourage hop-perspective semantic interactions by adopting multi-hop input-rich reconstruction while supporting mini-batch training. Despite the rationales of the above designs, we still observe some limitations (e.g., sub-optimal generalizability and training instability), potentially due to the implicit gap between the task-triviality and input-richness of reconstruction. Therefore, to alleviate task-triviality and fully unleash the potential of our framework, we further propose a combined fine-grained loss function, which generalizes the existing ones and significantly improves the difficulties of reconstruction tasks, thus naturally alleviating over-fitting. Extensive experiments on eight benchmarks demonstrate that our method comprehensively outperforms many state-of-the-art counterparts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Keyword ：
Graph Masked Auto-Encoders Graph Masked Auto-Encoders Graph Neural Networks Graph Neural Networks Graph Representation Learning Graph Representation Learning Self-Supervised Learning Self-Supervised Learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Shi, C. , Li, J. , Zhuang, J. et al. HopMAE: Self-supervised Graph Masked Auto-Encoders from a Hop Perspective [未知]. |
| MLA | Shi, C. et al. "HopMAE: Self-supervised Graph Masked Auto-Encoders from a Hop Perspective" [未知]. |
| APA | Shi, C. , Li, J. , Zhuang, J. , Yao, X. , Huang, Y. , Fu, Y.-G. . HopMAE: Self-supervised Graph Masked Auto-Encoders from a Hop Perspective [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Recent studies have shown that Graph Transformers (GTs) can be effective for specific graph-level tasks. However, when it comes to node classification, training GTs remains challenging, especially in semi-supervised settings with a severe scarcity of labeled data. Our paper aims to address this research gap by focusing on semi-supervised node classification. To accomplish this, we develop a curriculum-enhanced attention distillation method that involves utilizing a Local GT teacher and a Global GT student. Additionally, we introduce the concepts of in-class and out-of-class and then propose two improvements, out-of-class entropy and top-k pruning, to facilitate the student's out-of-class exploration under the teacher's in-class guidance. Taking inspiration from human learning, our method involves a curriculum mechanism for distillation that initially provides strict guidance to the student and gradually allows for more out-of-class exploration by a dynamic balance. Extensive experiments show that our method outperforms many state-of-the-art methods on seven public graph benchmarks, proving its effectiveness. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.
Keyword ：
Curricula Curricula Distillation Distillation Students Students
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Huang, Yisong , Li, Jin , Chen, Xinlong et al. TRAINING GRAPH TRANSFORMERS VIA CURRICULUM-ENHANCED ATTENTION DISTILLATION [C] . 2024 . |
| MLA | Huang, Yisong et al. "TRAINING GRAPH TRANSFORMERS VIA CURRICULUM-ENHANCED ATTENTION DISTILLATION" . (2024) . |
| APA | Huang, Yisong , Li, Jin , Chen, Xinlong , Fu, Yang-Geng . TRAINING GRAPH TRANSFORMERS VIA CURRICULUM-ENHANCED ATTENTION DISTILLATION . (2024) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Despite Graph neural networks’ significant performance gain over many classic techniques in various graph-related downstream tasks, their successes are restricted in shallow models due to over-smoothness and the difficulties of optimizations among many other issues. In this paper, to alleviate the over-smoothing issue, we propose a soft graph normalization method to preserve the diversities of node embeddings and prevent indiscrimination due to possible over-closeness. Combined with residual connections, we analyze the reason why the method can effectively capture the knowledge in both input graph structures and node features even with deep networks. Additionally, inspired by Curriculum Learning that learns easy examples before the hard ones, we propose a novel label-smoothing-based learning framework to enhance the optimization of deep GNNs, which iteratively smooths labels in an auxiliary graph and constructs many gradual non-smooth tasks for extracting increasingly complex knowledge and gradually discriminating nodes from coarse to fine. The method arguably reduces the risk of overfitting and generalizes better results. Finally, extensive experiments are carried out to demonstrate the effectiveness and potential of the proposed model and learning framework through comparison with twelve existing baselines including the state-of-the-art methods on twelve real-world node classification benchmarks. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Curricula Curricula Graphic methods Graphic methods Graph neural networks Graph neural networks Graph structures Graph structures Graph theory Graph theory Iterative methods Iterative methods Learning systems Learning systems
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Jin , Zhang, Qirong , Xu, Shuling et al. Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-Smoothness in Deep GNNs [C] . 2024 : 13528-13536 . |
| MLA | Li, Jin et al. "Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-Smoothness in Deep GNNs" . (2024) : 13528-13536 . |
| APA | Li, Jin , Zhang, Qirong , Xu, Shuling , Chen, Xinlong , Guo, Longkun , Fu, Yang-Geng . Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-Smoothness in Deep GNNs . (2024) : 13528-13536 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
