Neural Networks最新文献_第3页

Revisiting low-homophily for graph-based fraud detection

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-22 DOI: 10.1016/j.neunet.2025.107407

Tairan Huang , Qiutong Li , Cong Xu , Jianliang Gao , Zhao Li , Shichao Zhang

{"title":"Revisiting low-homophily for graph-based fraud detection","authors":"Tairan Huang , Qiutong Li , Cong Xu , Jianliang Gao , Zhao Li , Shichao Zhang","doi":"10.1016/j.neunet.2025.107407","DOIUrl":"10.1016/j.neunet.2025.107407","url":null,"abstract":"<div><div>The openness of Internet stimulates a large number of fraud behaviors which have become a huge threat. Graph-based fraud detectors have attracted extensive interest since the abundant structure information of graph data has proved effective. Conventional Graph Neural Network (GNN) approaches reveal fraudsters based on the homophily assumption. But fraudsters typically generate heterophilous connections and label-imbalanced neighborhood. Such behaviors deteriorate the performance of GNNs in fraud detection tasks due to the low homophily in graphs. Though some recent works have noticed the challenges, they either treat the heterophilous connections as homophilous ones or tend to reduce heterophily, which roughly ignore the benefits from heterophily. In this work, an integrated two-strategy framework HeteGAD is proposed to balance both homophily and heterophily information from neighbors. The key lies in explicitly shrinking intra-class distance and increasing inter-class segregation. Specifically, the Heterophily-aware Aggregation Strategy tease out the feature disparity on heterophilous neighbors and augment the disparity between representations with different labels. And the Homophily-aware Aggregation Strategy are devised to capture the homophilous information in global text and augment the representation similarity with the same label. Finally, two corresponding inter-relational attention mechanisms are incorporated to refine the procedure of modeling the interaction of multiple relations. Experiments are conducted to evaluate the proposed method with two real-world datasets, and demonstrate that the HeteGAD outperforms 11 state-of-the-art baselines for fraud detection.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107407"},"PeriodicalIF":6.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Restarted multiple kernel algorithms with self-guiding for large-scale multi-view clustering

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-22 DOI: 10.1016/j.neunet.2025.107409

Yongyan Guo, Gang Wu

{"title":"Restarted multiple kernel algorithms with self-guiding for large-scale multi-view clustering","authors":"Yongyan Guo, Gang Wu","doi":"10.1016/j.neunet.2025.107409","DOIUrl":"10.1016/j.neunet.2025.107409","url":null,"abstract":"<div><div>Multi-view clustering is a powerful approach for discovering underlying structures hidden behind diverse views of datasets. Most existing multi-view spectral clustering methods use fixed similarity matrices or alternately updated ones. However, the former often fall short in adaptively capturing relationships among different views, while the latter are often time-consuming and even impractical for large-scale datasets. To the best of our knowledge, there are no multi-view spectral clustering methods can both construct multi-view similarity matrices inexpensively and preserve the valuable clustering insights from previous cycles at the same time. To fill in this gap, we present a Sum-Ratio Multi-view Ncut model that share a common representation embedding for multi-view data. Based on this model, we propose a restarted multi-view multiple kernel clustering framework with self-guiding. To release the overhead, we use similarity matrices with strict block diagonal representation, and present an efficient multiple kernel selection technique. Comprehensive experiments on benchmark multi-view datasets demonstrate that, even using randomly generated initial guesses, the restarted algorithms can improve the clustering performances by 5–10 times for some popular multi-view clustering methods. Specifically, our framework offers a potential boosting effect for most of the state-of-the-art multi-view clustering algorithms at very little cost, especially for those with poor performances.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"187 ","pages":"Article 107409"},"PeriodicalIF":6.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motif and supernode-enhanced gated graph neural networks for session-based recommendation

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-21 DOI: 10.1016/j.neunet.2025.107406

Ronghua Lin , Chang Liu , Hao Zhong , Chengzhe Yuan , Guohua Chen , Yuncheng Jiang , Yong Tang

{"title":"Motif and supernode-enhanced gated graph neural networks for session-based recommendation","authors":"Ronghua Lin , Chang Liu , Hao Zhong , Chengzhe Yuan , Guohua Chen , Yuncheng Jiang , Yong Tang","doi":"10.1016/j.neunet.2025.107406","DOIUrl":"10.1016/j.neunet.2025.107406","url":null,"abstract":"<div><div>Session-based recommendation systems aim to predict users’ next interactions based on short-lived, anonymous sessions, a challenging yet vital task due to the sparsity and dynamic nature of user behavior. Existing Graph Neural Network (GNN)-based methods primarily focus on the session graphs while overlooking the influence of micro-structures and user behavior patterns. To address these limitations, we propose a Motif and Supernode-Enhanced Session-based Recommender System (MSERS), which constructs a global session graph, identifies and encodes motifs as supernodes, and reintegrates them into the global graph to enrich its topology and better represent item dependencies. By employing supernode-enhanced Gated Graph Neural Networks (GGNN), MSERS captures both long-term and latent item dependencies, significantly improving session representations. Extensive experiments on two real-world datasets demonstrate the superiority of MSERS over baseline methods, providing robust insights into the role of micro-structures in session-based recommendations.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"187 ","pages":"Article 107406"},"PeriodicalIF":6.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AAPMatcher: Adaptive attention pruning matcher for accurate local feature matching

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-21 DOI: 10.1016/j.neunet.2025.107403

Xuan Fan , Sijia Liu , Shuaiyan Liu , Lijun Zhao , Ruifeng Li

{"title":"AAPMatcher: Adaptive attention pruning matcher for accurate local feature matching","authors":"Xuan Fan , Sijia Liu , Shuaiyan Liu , Lijun Zhao , Ruifeng Li","doi":"10.1016/j.neunet.2025.107403","DOIUrl":"10.1016/j.neunet.2025.107403","url":null,"abstract":"<div><div>Local feature matching, which seeks to establish correspondences between two images, serves as a fundamental component in numerous computer vision applications, such as camera tracking and 3D mapping. Recently, Transformer has demonstrated remarkable capability in modeling accurate correspondences for the two input sequences owing to its long-range context integration capability. Whereas, indiscriminate modeling in traditional transformers inevitably introduces noise and includes irrelevant information which can degrade the quality of feature representations. Towards this end, we introduce an <em>adaptive attention pruning matcher for accurate local feature matching (AAPMatcher)</em>, which is designed for robust and accurate local feature matching. We overhaul the traditional uniform feature extraction for sequences by introducing the adaptive pruned transformer (APFormer), which adaptively retains the most profitable attention values for feature consolidation, enabling the network to obtain more useful feature information while filtering out useless information. Moreover, considering the fixed combination of self- and cross-APFormer greatly limits the flexibility of the network, we propose a two-stage <em>adaptive hybrid attention strategy (AHAS)</em>, which achieves the optimal combination for APFormers in a coarse to fine manner. Benefiting from the clean feature representations and the optimal combination of APFormers, AAPMatcher exceeds the state-of-the-art approaches over multiple benchmarks, including pose estimation, homography estimation, and visual localization.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107403"},"PeriodicalIF":6.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive node-level weighted learning for directed graph neural network

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-21 DOI: 10.1016/j.neunet.2025.107393

Jincheng Huang , Xiaofeng Zhu

引用次数: 0

More signals matter to detection: Integrating language knowledge and frequency representations for boosting fine-grained aircraft recognition

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-21 DOI: 10.1016/j.neunet.2025.107402

Xueru Xu, Zhong Chen, Yuxin Hu, Guoyou Wang

{"title":"More signals matter to detection: Integrating language knowledge and frequency representations for boosting fine-grained aircraft recognition","authors":"Xueru Xu, Zhong Chen, Yuxin Hu, Guoyou Wang","doi":"10.1016/j.neunet.2025.107402","DOIUrl":"10.1016/j.neunet.2025.107402","url":null,"abstract":"<div><div>As object detection tasks progress rapidly, fine-grained detection flourishes as a promising extension. Fine-grained recognition naturally demands high-quality detail signals; however, existing fine-grained detectors, built upon the mainstream detection paradigm, struggle to simultaneously address the challenges of insufficient original signals and the loss of critical signals, resulting in inferior performance. We argue that language signals with advanced semantic knowledge can provide valuable information for fine-grained objects, as well as the frequency domain exhibits greater flexibility in suppressing and enhancing signals; then, we propose a fine-grained aircraft detector by integrating language knowledge and frequency representations into the one-stage detection paradigm. Concretely, by considering both original signals and deep feature signals, we develop three components, including an adaptive frequency augmentation branch (AFAB), a content-aware global features intensifier (CGFI), and a fine-grained text–image interactive feeder (FTIF), to facilitate perceiving and retaining critical signals throughout pivotal detection stages. The AFAB adaptively processes image patches according to their frequency characteristics in the Fourier domain, thus thoroughly mining critical visual content in the data space; the CGFI employs content-aware frequency filtering to enhance global features, allowing for generating an information-rich feature space; the FTIF introduces text knowledge to describe visual differences among fine-grained categories, conveying robust semantic priors from language signals to visual spaces via multimodal interaction for information supplement. Extensive experiments conducted on optical and SAR images demonstrate the superior performance of the proposed fine-grained detector, especially the FTIF, which can be plugged into most existing one-stage detectors to boost their fine-grained recognition performance significantly.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"187 ","pages":"Article 107402"},"PeriodicalIF":6.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intra-class progressive and adaptive self-distillation

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-21 DOI: 10.1016/j.neunet.2025.107404

Jianping Gou , Jiaye Lin , Lin Li , Weihua Ou , Baosheng Yu , Zhang Yi

{"title":"Intra-class progressive and adaptive self-distillation","authors":"Jianping Gou , Jiaye Lin , Lin Li , Weihua Ou , Baosheng Yu , Zhang Yi","doi":"10.1016/j.neunet.2025.107404","DOIUrl":"10.1016/j.neunet.2025.107404","url":null,"abstract":"<div><div>In recent years, knowledge distillation (KD) has become widely used in compressing models, training compact and efficient students to reduce computational load and training time due to the increasing parameters in deep neural networks. To minimize training costs, self-distillation has been proposed, with methods like offline-KD and online-KD requiring pre-trained teachers and multiple networks. However, these self-distillation methods often overlook feature knowledge and category information. In this paper, we introduce Intra-class Progressive and Adaptive Self-Distillation (IPASD), which transfers knowledge from the front to the back in adjacent epochs. This method extracts class-typical features and promotes compactness within classes. By integrating feature-level and logits-level knowledge into strong teacher knowledge and using ground-truth labels as supervision signals, we adaptively optimize the model. We evaluated IPASD on CIFAR-10, CIFAR-100, Tiny ImageNet, Plant Village datasets, and ImageNet showing its superiority over state-of-the-art self-distillation methods in knowledge transfer and model compression. Our codes are available at: <span><span>https://github.com/JLinye/IPASD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107404"},"PeriodicalIF":6.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structure information preserving domain adaptation network for fault diagnosis of Sucker Rod Pumping systems

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-21 DOI: 10.1016/j.neunet.2025.107392

Xiaohua Gu , Fei Lu , Liping Yang , Kan Wang , Lusi Li , Guang Yang , Yiling Sun

{"title":"Structure information preserving domain adaptation network for fault diagnosis of Sucker Rod Pumping systems","authors":"Xiaohua Gu , Fei Lu , Liping Yang , Kan Wang , Lusi Li , Guang Yang , Yiling Sun","doi":"10.1016/j.neunet.2025.107392","DOIUrl":"10.1016/j.neunet.2025.107392","url":null,"abstract":"<div><div>Fault diagnosis is of great importance to the reliability and security of Sucker Rod Pumping (SRP) oil production system. With the development of digital oilfield, data-driven deep learning SRP fault diagnosis has become the development trend of oilfield system. However, due to the different working conditions, time periods, and areas, the fault diagnosis models trained from certain SRP data do not consider the statistical discrepancy of different SRP systems, resulting in insufficient generalization. To consider the fault diagnosis and generalization performances of deep models at the same time, this paper proposes a Structure Information Preserving Domain Adaptation Network (SIP-DAN) for SRP fault diagnosis. Different from the usual domain adaptation methods, SIP-DAN divides the source domain data into different subdomains according to the fault categories of the source domain, and then realizes structure information preserving domain adaptation through subdomains alignment of the source domain and the target domain. Due to the lack of fault category information in the target domain, we designed a Classifier Voting Assisted Alignment (CVAA) mechanism. The target domain data are divided into clusters using fuzzy clustering algorithm. Then, fault diagnosis classifier trained in source domain is employed to classify the samples in each cluster, and the majority voting principle is used to assign pseudo-labels to each cluster in the target domain. With these pseudo-labels, source and target subdomains alignment is carried out by optimizing the Local Maximum Mean Discrepancy (LMMD) loss to achieve fine-grained domain adaptation. Experimental results illustrate that the proposed method is better than the existing methods in fault diagnosis of SRP systems.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107392"},"PeriodicalIF":6.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Central loss guides coordinated Transformer for reliable anatomical landmark detection

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-21 DOI: 10.1016/j.neunet.2025.107391

Qikui Zhu , Yihui Bi , Jie Chen , Xiangpeng Chu , Danxin Wang , Yanqing Wang

{"title":"Central loss guides coordinated Transformer for reliable anatomical landmark detection","authors":"Qikui Zhu , Yihui Bi , Jie Chen , Xiangpeng Chu , Danxin Wang , Yanqing Wang","doi":"10.1016/j.neunet.2025.107391","DOIUrl":"10.1016/j.neunet.2025.107391","url":null,"abstract":"<div><div>Heatmap-based anatomical landmark detection is still facing two unresolved challenges: (1) inability to accurately evaluate the distribution of heatmap; (2) inability to effectively exploit global spatial structure information. To address the computational inability challenge, we propose a novel position-aware and sample-aware central loss. Specifically, our central loss can absorb position information, enabling accurate evaluation of the heatmap distribution. More advanced is that our central loss is sample-aware, which can adaptively distinguish easy and hard samples and make the model more focused on hard samples while solving the challenge of extreme imbalance between landmarks and non-landmarks. To address the challenge of ignoring structure information, a Coordinated Transformer, called CoorTransformer, is proposed, which establishes long-range dependencies under the guidance of landmark coordinate information, making the attention more focused on the sparse landmarks while taking advantage of global spatial structure. Furthermore, CoorTransformer can speed up convergence, effectively avoiding the defect that Transformers have difficulty converging in sparse representation learning. Using the advanced CoorTransformer and central loss, we propose a generalized detection model that can handle various scenarios, inherently exploiting the underlying relationship between landmarks and incorporating rich structural knowledge around the target landmarks. We analyzed and evaluated CoorTransformer and central loss on three challenging landmark detection tasks. The experimental results show that our CoorTransformer outperforms state-of-the-art methods, and the central loss significantly improves the model’s performance with <span><math><mi>p</mi></math></span>-values <span><math><mrow><mo><</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>. The source code of this work is available at the <span><span>GitHub repository</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"187 ","pages":"Article 107391"},"PeriodicalIF":6.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mathematical expression exploration with graph representation and generative graph neural network

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-21 DOI: 10.1016/j.neunet.2025.107405

Jingyi Liu , Weijun Li , Lina Yu , Min Wu , Wenqiang Li , Yanjie Li , Meilan Hao

{"title":"Mathematical expression exploration with graph representation and generative graph neural network","authors":"Jingyi Liu , Weijun Li , Lina Yu , Min Wu , Wenqiang Li , Yanjie Li , Meilan Hao","doi":"10.1016/j.neunet.2025.107405","DOIUrl":"10.1016/j.neunet.2025.107405","url":null,"abstract":"<div><div>Symbolic Regression (SR) methods in tree representations have exhibited commendable outcomes across Genetic Programming (GP) and deep learning search paradigms. Nonetheless, the tree representation of mathematical expressions occasionally embodies redundant substructures. Representing expressions as computation graphs is more succinct and intuitive through graph representation. Despite its adoption in evolutionary strategies within SR, deep learning paradigms remain under-explored. Acknowledging the profound advancements of deep learning in tree-centric SR approaches, we advocate for addressing SR tasks using the Directed Acyclic Graph (DAG) representation of mathematical expressions, complemented by a generative graph neural network. We name the proposed method as <em><strong>Graph</strong>-based <strong>D</strong>eep <strong>S</strong>ymbolic <strong>R</strong>egression (GraphDSR)</em>. We vectorize node types and employ an adjacent matrix to delineate connections. The graph neural networks craft the DAG incrementally, sampling node types and graph connections conditioned on previous DAG at every step. During each sample step, the valid check is implemented to avoid meaningless sampling, and four domain-agnostic constraints are adopted to further streamline the search. This process culminates once a coherent expression emerges. Constants undergo optimization by SGD and BFGS algorithms, and rewards refine the graph neural network through reinforcement learning. A comprehensive evaluation across 110 benchmarks underscores the potency of our approach.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"187 ","pages":"Article 107405"},"PeriodicalIF":6.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0