Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Learning from a limited number of samples is challenging since the learned model can
easily become overfitted based on the biased distribution formed by only a few training …

Prevailing methods for graphs require abundant label and edge information for learning.
When data for a new task are scarce, meta-learning can learn from prior experiences and …

Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such
as social network analysis, financial fraud detection, and drug discovery. As a central …

Few-shot classification aims to recognize unseen classes when presented with only a small
number of samples. We consider the problem of multi-domain few-shot image classification …

Learning personalized models for user-customized computer-vision tasks is challenging due
to the limited private-data and computation available on each edge device. Decentralized …

Predicting urban traffic is of great importance to intelligent transportation systems and public
safety, yet is very challenging in three aspects: 1) complex spatio-temporal correlations of …