7 citations · 17 across the 11 of their papers we have counts for
11 papers
How Faithful are Self-Explainable GNNs?
Marc Christiansen, Lea Villadsen, Zhiqiang Zhong +2
Self-explainable deep neural networks are a recent class of models that can output ante-hoc local explanations that are faithful to the model's reasoning, and as such represent a s…
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
Zhiqiang Zhong, Anastasia Barkova, Davide Mottin
The integration of Artificial Intelligence (AI) into the field of drug discovery has been a growing area of interdisciplinary scientific research. However, conventional AI models a…
"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media
Ninghan Chen, Xihui Chen, Zhiqiang Zhong +1
To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their…
Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
Zhiqiang Zhong, Sergey Ivanov, Jun Pang
Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with…
Unsupervised Network Embedding Beyond Homophily
Zhiqiang Zhong, Guadalupe Gonzalez, Daniele Grattarola +1
Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a hom…
The Burden of Being a Bridge: Analysing Subjective Well-Being of Twitter Users during the COVID-19 Pandemic
Ninghan Chen, Xihui Chen, Zhiqiang Zhong +1
The outbreak of the COVID-19 pandemic triggers infodemic over online social media, which significantly impacts public health around the world, both physically and psychologically.…
From #Jobsearch to #Mask: Improving COVID-19 Cascade Prediction with Spillover Effects
Ninghan Chen, Xihui Chen, Zhiqiang Zhong +1
An information outbreak occurs on social media along with the COVID-19 pandemic and leads to infodemic. Predicting the popularity of online content, known as cascade prediction, al…
Personalised Meta-path Generation for Heterogeneous GNNs
Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous in…
Multi-grained Semantics-aware Graph Neural Networks
Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Graph Neural Networks (GNNs) are powerful techniques in representation learning for graphs and have been increasingly deployed in a multitude of different applications that involve…
Hierarchical Message-Passing Graph Neural Networks
Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Graph Neural Networks (GNNs) have become a prominent approach to machine learning with graphs and have been increasingly applied in a multitude of domains. Nevertheless, since most…
An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
Ninghan Chen, Zhiqiang Zhong, Jun Pang
The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platfo…