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Found 15 papers, 8 papers with code
Benchmarking Large Language Models for Molecule Prediction Tasks
Our investigation reveals several key insights: Firstly, LLMs generally lag behind ML models in achieving competitive performance on molecule tasks, particularly when compared to models adept at capturing the geometric structure of molecules, highlighting the constrained ability of LLMs to comprehend graph data.
EvolMPNN: Predicting Mutational Effect on Homologous Proteins by Evolution Encoding
Predicting protein properties is paramount for biological and medical advancements.
Harnessing Large Language Models as Post-hoc Correctors
We show that, through our proposed training-free framework LlmCorr, an LLM can work as a post-hoc corrector to propose corrections for the predictions of an arbitrary ML model.
On the Robustness of Post-hoc GNN Explainers to Label Noise
Proposed as a solution to the inherent black-box limitations of graph neural networks (GNNs), post-hoc GNN explainers aim to provide precise and insightful explanations of the behaviours exhibited by trained GNNs.
How Faithful are Self-Explainable GNNs?
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 step forward toward filling the gap between expressiveness and interpretability.
Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices
Therefore, this study provides an effective solution for resource-constraint IoT smart health devices in PPG artifact detection.
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability
The integration of Artificial Intelligence (AI) into the field of drug discovery has been a growing area of interdisciplinary scientific research.
"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media
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 changes.
Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
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 a shallow neural network can achieve comparable performance to GNNs in semi-supervised node classification on graphs with high homophily.
Unsupervised Network Embedding Beyond Homophily
Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily.
From #Jobsearch to #Mask: Improving COVID-19 Cascade Prediction with Spillover Effects
Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 related messages.
Social and Information Networks Computers and Society
Personalised Meta-path Generation for Heterogeneous GNNs
To fully unleash the power of HGRL, we present a novel framework, Personalised Meta-path based Heterogeneous Graph Neural Networks (PM-HGNN), to jointly generate meta-paths that are personalised for each individual node in a HIN and learn node representations for the target downstream task like node classification.
Multi-grained Semantics-aware Graph Neural Networks
Specifically, a differentiable pooling operator is proposed to adaptively generate a multi-grained structure that involves meso- and macro-level semantic information in the graph.
Hierarchical Message-Passing Graph Neural Networks
To deal with these two issues, we propose a novel Hierarchical Message-passing Graph Neural Networks framework.
An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs).
