Deep graph similarity learning: A survey
In many domains where data are represented as graphs, learning a similarity metric among
graphs is considered a key problem, which can further facilitate various learning tasks, such …
graphs is considered a key problem, which can further facilitate various learning tasks, such …
Graph interpretation, summarization and visualization techniques: a review and open research issues
Graphs has been a ubiquitous way of representing heterogeneous data. There are many
studies focused on graph learning highlighting the approaches for graph data extraction …
studies focused on graph learning highlighting the approaches for graph data extraction …
Message passing attention networks for document understanding
Graph neural networks have recently emerged as a very effective framework for processing
graph-structured data. These models have achieved state-of-the-art performance in many …
graph-structured data. These models have achieved state-of-the-art performance in many …
Transfer learning convolutional neural network for sleep stage classification using two-stage data fusion framework
The most important part of sleep quality assessment is the classification of sleep stages,
which helps to diagnose sleep-related disease. In the traditional sleep staging method …
which helps to diagnose sleep-related disease. In the traditional sleep staging method …
SGCN: A scalable graph convolutional network with graph-shaped kernels and multi-channels
Graph neural networks (GNNs) have demonstrated great success in graph processing.
However, current message-passing-based GNNs have limitations in terms of feature …
However, current message-passing-based GNNs have limitations in terms of feature …
Unsupervised event graph representation and similarity learning on biomedical literature
The automatic extraction of biomedical events from the scientific literature has drawn keen
interest in the last several years, recognizing complex and semantically rich graphical …
interest in the last several years, recognizing complex and semantically rich graphical …
Employing robotics and deep learning in underground leak detection
Leaks in water distribution networks (WDNs) produce significant economic losses. These
leaks from underground pipelines affect the surrounding environment in different ways that …
leaks from underground pipelines affect the surrounding environment in different ways that …
Manufacturing feature recognition with a 2D convolutional neural network
Feature recognition is critical to connect CAX tools in automation via the extract of significant
geometric information from CAD models. However, to extract meaningful geometric …
geometric information from CAD models. However, to extract meaningful geometric …
DeepSIM: a novel deep learning method for graph similarity computation
B Liu, Z Wang, J Zhang, J Wu, G Qu - Soft Computing, 2024 - Springer
Graphs are widely used to model real-life information, where graph similarity computation is
one of the most significant applications, such as inferring the properties of a compound …
one of the most significant applications, such as inferring the properties of a compound …
Graph-hist: Graph classification from latent feature histograms with application to bot detection
Neural networks are increasingly used for graph classification in a variety of contexts. Social
media is a critical application area in this space, however the characteristics of social media …
media is a critical application area in this space, however the characteristics of social media …