527 citations · 1.6k across the 31 of their papers we have counts for
6 papers · 1 filter
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
Haozhen Zhang, Le Yu, Xi Xiao +4
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failin…
Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs
Haozhen Zhang, Xueting Han, Xi Xiao +1
Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality,…
Diversified Multiscale Graph Learning with Graph Self-Correction
Yuzhao Chen, Yatao Bian, Jiying Zhang +4
Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering…
On Self-Distilling Graph Neural Network
Yuzhao Chen, Yatao Bian, Xi Xiao +3
Recently, the teacher-student knowledge distillation framework has demonstrated its potential in training Graph Neural Networks (GNNs). However, due to the difficulty of training o…
Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
Tian Bian, Xi Xiao, Tingyang Xu +4
Graph Identification (GI) has long been researched in graph learning and is essential in certain applications (e.g. social community detection). Specifically, GI requires to predic…
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
Bowen Zhao, Xi Xiao, Wanpeng Zhang +2
Principal Component Analysis (PCA) is a popular tool for dimensionality reduction and feature extraction in data analysis. There is a probabilistic version of PCA, known as Probabi…