Recent advances in single-cell RNA sequencing (scRNA-seq) technologies provide a great
opportunity to study gene expression at cellular resolution, and the scRNA-seq data has …

The increase in the source and size of encrypted network traffic brings significant challenges
for network traffic analysis. The challenging problem in the encrypted traffic classification …

Graph clustering is a fundamental task in data analysis and has attracted considerable
attention in recommendation systems, mapping knowledge domain, and biological science …

In recent years, substantial progress has been made on Graph Convolutional Networks
(GCNs). However, the computing of GCN usually requires a large memory space for keeping …

This paper introduces Multi-Level feature learning alongside the Embedding layer of
Convolutional Autoencoder (CAE-MLE) as a novel approach in deep clustering. We use …

Text clustering is a fundamental task that finds groups of similar texts in the corpus. Deep
learning based models can capture the semantic and syntactic information in local word …

Graph Identification (GI) has long been researched in graph learning and is essential in
certain applications (eg social community detection). Specifically, GI requires to predict the …

Abstract Graph Neural Networks (GNNs) have been increasingly deployed in a multitude of
different applications that involve node-wise and graph-level tasks. The existing literature …

Short text analysis has become an essential task for mining textual data in diverse social
media platforms (eg, Twitter, Google News). However, most of the existing text modeling …