Yafang Li

• Beijing Jiaotong University

Pollen image classification is crucial for understanding allergic reactions and environmental impacts. In this study, we propose RESwinT, an enhanced deep learning model specifically designed for pollen image classification. RESwinT incorporates a parallel window transformer block with contextual information aggregation to expand the receptive fiel...

The rapid dissemination of unverified information through social platforms like Twitter poses considerable dangers to societal stability. Identifying real versus fake claims is challenging, and previous work on rumor detection methods often fails to effectively capture propagation structure features. These methods also often overlook the presence o...

Graph embedding has been extensively studied in the literature and is widely used in various applications such as drug discovery, social network analysis, and natural language processing. However, existing approaches ignore the attribute information or are limited to learning graph representations at certain graph scales without considering the lay...

Graph representation learning is the encoding of graph nodes into a low-dimensional representation space, which can effectively improve graph information representation while reducing the information dimensionality. To overcome the heavy reliance on label information of previous graph representation learning, the graph contrastive learning method h...

The integrity of real-time data streams has not been solved for a long time and has gradually become a difficult problem in the field of data security. Most of the current data integrity verification schemes are constructed using cryptographic algorithms with complex computation, which cannot be directly applied to real-time stream computing system...

Recently, deep learning for hyperspectral image classification has been successfully applied, and some convolutional neural network (CNN)-based models already achieved attractive classification results. Since hyperspectral data is a spectral-spatial cube data that can generally be considered as sequential data along with the spectral dimension, CNN...

Hyperspectral image (HSI) classification attempts to classify each pixel, which is an important means of obtaining land–cover knowledge. Hyperspectral images are cubic data with spectral–spatial knowledge and can generally be considered as sequential data alongside spectral dimension. Unlike convolutional neural networks (CNNs), which mainly focus...

Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, explosive growth in HSIs’ scale and dimensions causes “Curse of dimensionality” and “Hughes phenomenon”. Dimensionality reduction has become an important means to overcome the “Curse of dimens...

Semi-supervised community detection has gained a lot of attention by leveraging side information for better understanding network topologies. However, most of existing works select side information in a random manner. They usually require a great amount of side information to significantly improve the performance of community detection. Besides, th...

Classification of Hyperspectral Images (HSIs) has gained attention for the past few decades. In remote sensing image classification, the labeled samples are insufficient or hard to obtain; however, the unlabeled ones are frequently rich and of a vast number. When there are no sufficient labeled samples, overfitting may occur. To resolve the overfit...

Attributed graphs have attracted much attention in recent years. Different from conventional graphs, attributed graphs involve two different types of heterogeneous information, i.e., structural information, which represents the links between the nodes, and attribute information on each of the nodes. Clustering on attributed graphs usually requires...

Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes. Iden...

K-means is a simple and efficient clustering algorithm to detect communities in networks. However, it may suffer from a bad choice of initial seeds (also called centers) that seriously affect the clustering accuracy and the convergence rate. Additionally, in K-means, the number of communities should be specified in advance. Till now, it is still an...