Explaining the explainers in graph neural networks: a comparative study
Following a fast initial breakthrough in graph based learning, Graph Neural Networks
(GNNs) have reached a widespread application in many science and engineering fields …
(GNNs) have reached a widespread application in many science and engineering fields …
A survey on text classification: From traditional to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Substructure aware graph neural networks
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning,
conventional GNNs struggle to break through the upper limit of the expressiveness of first …
conventional GNNs struggle to break through the upper limit of the expressiveness of first …
A survey on text classification: From shallow to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Ood-gnn: Out-of-distribution generalized graph neural network
Graph neural networks (GNNs) have achieved impressive performance when testing and
training graph data come from identical distribution. However, existing GNNs lack out-of …
training graph data come from identical distribution. However, existing GNNs lack out-of …
Popularity-aware and diverse web APIs recommendation based on correlation graph
The ever-increasing web application programming interfaces (APIs) in various service-
sharing communities (eg, ProgrammableWeb. com and Mashape. com) have enabled …
sharing communities (eg, ProgrammableWeb. com and Mashape. com) have enabled …
RA-HGNN: Attribute completion of heterogeneous graph neural networks based on residual attention mechanism
Z Zhao, Z Liu, Y Wang, D Yang, W Che - Expert Systems with Applications, 2024 - Elsevier
Heterogeneous graphs, which are also called heterogeneous information networks, analyze
the different types of nodes in an information network and the different types of links between …
the different types of nodes in an information network and the different types of links between …
Privacy-aware point-of-interest category recommendation in internet of things
In location-based social networks (LBSNs), extensive user check-in data incorporating user
preferences for location is collected through Internet of Things devices, including cell …
preferences for location is collected through Internet of Things devices, including cell …
Time-aware missing healthcare data prediction based on ARIMA model
L Kong, G Li, W Rafique, S Shen, Q He… - … ACM transactions on …, 2022 - ieeexplore.ieee.org
Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose
meters, electrocardiographs), which results in the generation of large amounts of data …
meters, electrocardiographs), which results in the generation of large amounts of data …
Higher-order clustering and pooling for graph neural networks
A Duval, F Malliaros - Proceedings of the 31st ACM international …, 2022 - dl.acm.org
Graph Neural Networks achieve state-of-the-art performance on a plethora of graph
classification tasks, especially due to pooling operators, which aggregate learned node …
classification tasks, especially due to pooling operators, which aggregate learned node …