Network embedding maps a network into low-dimensional Euclidean space, and thus facilitate many analysis tasks, such as node classification, link prediction community detection etc, by utilizing machine learning methods. In social networks, we may pay special attention to user privacy, would like prevent some target nodes from being identified methods in certain cases. Inspired successful adversarial attack on deep models, propose framework generate networks based the gradient information...

Dynamic link prediction is a research hot in complex networks area, especially for its wide applications biology, social network, economy and industry. Compared with static prediction, dynamic one much more difficult since network structure evolves over time. Currently most researches focus on which cannot achieve expected performance network. Aiming at low AUC, high Error Rate, add/remove difficulty, we propose GC-LSTM, Graph Convolution Network (GC) embedded Long Short Term Memory (LTSM),...

Recommender systems are becoming more and important in our daily lives. However, traditional recommendation methods challenged by data sparsity efficiency, as the numbers of users, items, interactions between two many real-world applications increase fast. In this paper, we propose a novel clustering recommender system based on node2vec technology rich information network, namely, N2VSCDNNR, to solve these challenges. particular, use bipartite network construct user-item represent among...

Recently, a graph neural network (GNN) was proposed to analyze various graphs/networks, which has been proven outperform many other analysis methods. However, it is also shown that such state-of-the-art methods suffer from adversarial attacks, i.e., carefully crafted networks with slight perturbation on clean one may invalid these lots of applications, as embedding, node classification, link prediction, and community detection. Adversarial training testified an efficient defense strategy...

This paper proposes a framework for layout evaluation of urban public sports facilities. First, the buffer analysis method is used to measure service level The study findings indicate that overall facilities presents spatial characteristics central agglomeration, and value diffuses outward from high low. There evident heterogeneity in Hangzhou. Second, Gini coefficient, Lorenz curve, location entropy are employed equity distribution among units intradistrict disparity. results show mismatch...

Deep neural network has shown remarkable performance in solving computer vision and some graph evolved tasks, such as node classification link prediction. However, the vulnerability of deep model also been revealed by carefully designed adversarial examples generated various attack methods. With wider application complex analysis, this paper we define formulate prediction problem put forward a novel iterative gradient (IGA) based on information trained auto-encoder (GAE). To our best...

Machine learning has been successfully applied to complex network analysis in various areas, and graph neural networks (GNNs) based methods outperform others. Recently, adversarial attack on attracted special attention since carefully crafted with slight perturbations clean may invalid lots of applications, such as node classification, link prediction, community detection etc. Such attacks are easily constructed serious security threat analyze methods, including traditional deep models. To...

Data quality is paramount in today's data-driven world, especially the era of generative AI. Dirty data with errors and inconsistencies usually leads to flawed insights, unreliable decision-making, biased or low-quality outputs from models. The study repairing erroneous has gained significant importance. Existing repair algorithms differ information utilization, problem settings, are tested limited scenarios. In this paper, we compare summarize these a driven information-based taxonomy. We...

In networks, a link prediction task aims at learning potential relations between nodes to predict unknown linkage states. At present, most methods are used process static networks. These cannot produce good results for dynamic However, networks in the real world, vertices and links of these change over time. Dynamic (DLP) has attracted more attention as it can better mimic evolution nature Inspired by successful applications generative adversarial network generating fake images, which...

Network embedding plays an important role in various network applications, such as node classification and link prediction. Lots of structure-based methods have been proposed. Yet, they suffer from unsteady performances due to the parameter sensitivity. How extract valuable attribute information networks with less influence is still a challenge. In this paper, we propose novel algorithm, named adaptive particle swarm optimization (PSO-ANE), which based on second-order dynamic random walk PSO...

Due to factors that include systematic sensor delay, it is difficult for synthetic aperture radar (SAR) satellite stereo positioning meet the requirements of large-scale mapping. In traditional methods, ground control points (GCP) are required eliminate errors. this study, a method proposed improve accuracy without GCP. First, influencing Gaofen-3 (GF-3) were analysed. The errors divided into fixed errors, spatial and temporal variation random Subsequently, geometric error compensation model...

With the wider applications of mobile devices, network-based recommendation has attracted more attentions than ever. At present, most research recommender system is on basis either graph theory or algebraic methods. However almost all these algorithms are aiming at static history data, which cannot meet demand precise online for dynamic real-time emerging data. Besides, lots traditional suffered from high time complexity and parameter tuning difficulty. Here, we propose a personalized...

The low-resolution ultrasound images have poor visual effects. Herein we propose a method for generating clearer intravascular based on super-resolution reconstruction combined with generative adversarial networks. We used the networks to generate by generator and estimate authenticity of discriminator. Specifically, image was passed through sub-pixel convolution layer r2-feature channels maps in same size, followed realignment corresponding pixels each feature map into r ×r sub-blocks,...

Spectral clustering algorithm (SCA) is one of the widely used algorithms (CAs), which proved to be efficient in many applications including unsupervised image identification and gene prediction. However, most SCAs are confronted with several problems: 1) It difficult for handle multi-scale data sets; 2) set cluster number advance various applications; 3) also choose appropriate eigenvectors reflect distribution; 4) Moreover, sensitive parameters. To these problems, we propose a novel SCA...

Recommender systems are becoming part of our lives and make many online getting smarter. However, with the fast increasing amount users, items, interactions between two, traditional recommendation methods encounter challenges sparsity efficiency. Clustering-based is a common strategy to overcome these problems, but them rely heavily on parameter settings. In this work, we put forward an improved spectral clustering-based collaborative filtering framework based node2vec algorithm, named as...

Some alarm correlation rules were discovered from the databases of telecommunication network management systems by using technology association rule mining and sequence analysis. The generation release information alarms was recognized extracted sequences. between time clear used to reorganize data so that better result could be obtained. This paper proposes a method combine correlated form an transaction. not only solves problem asynchrony in network, but also discards many redundant data,...

Objective While current multimodal approaches in the diagnosis and severity assessment of pneumonia demonstrate remarkable performance, they frequently overlook issue modality absence—a common challenge clinical practice. Thus, we present robust transformer (RMT) model, crafted to bridge this gap. The RMT model aims enhance accuracy situations with incomplete data, thereby ensuring it meets complex needs real-world settings. Method leverages integrating X-ray images text data through a...