Graph classification based on graph set reconstruction and graph kernel feature reduction
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.03.029
Abstract: Abstract Graph, a kind of structured data, is widely used to model complex relationships among objects, and has been used in various of scientific and engineering fields, such as bioinformatics, network intrusion detection, social network,… read more here.
Keywords: classification; graph set; graph classification; graph kernel ... See more keywords
Big graph classification frameworks based on Extreme Learning Machine
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.11.035
Abstract: Abstract Graph data analysis is a hot topic in recent research area. Graph classification is one of the most important graph data analysis problems, which choose the most probable class labels of graphs using models… read more here.
Keywords: classification; graph data; big graph; graph classification ... See more keywords
Feature reduction based on semantic similarity for graph classification
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DOI: 10.1016/j.neucom.2020.02.047
Abstract: Abstract Classification and recognition of graph data are crucial problems in many fields, such as bioinformatics, chemoinformatics and data mining. In graph kernel-based classification methods, the similarity among substructures is not fully considered; in addition,… read more here.
Keywords: graph classification; feature reduction; classification; similarity ... See more keywords
Multistructure Graph Classification Method With Attention-Based Pooling
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Computational Social Systems"
DOI: 10.1109/tcss.2022.3169219
Abstract: Graph neural networks (GNNs) have achieved effective performance in many graph-related tasks involving recommendation systems, social networks, and bioinformatics. Recent studies have proposed several graph pooling operators to obtain graph-level representations from node representations. Nevertheless,… read more here.
Keywords: graph; method; attention; multistructure graph ... See more keywords
$K$ -Ary Tree Hashing for Fast Graph Classification
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2017.2782278
Abstract: Existing graph classification usually relies on an exhaustive enumeration of substructure patterns, where the number of substructures expands exponentially w.r.t. with the size of the graph set. Recently, the Weisfeiler-Lehman (WL) graph kernel has achieved… read more here.
Keywords: graph classification; tex math; alternatives inline; inline formula ... See more keywords
Multiple Structure-View Learning for Graph Classification
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DOI: 10.1109/tnnls.2017.2703832
Abstract: Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification… read more here.
Keywords: classification; multiple structure; view; graph classification ... See more keywords
Walk-Steered Convolution for Graph Classification
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2019.2956095
Abstract: Graph classification is a fundamental but challenging issue for numerous real-world applications. Despite recent great progress in image/video classification, convolutional neural networks (CNNs) cannot yet cater to graphs well because of graphical non-Euclidean topology. In… read more here.
Keywords: graph classification; convolution; walk steered; graph ... See more keywords
A Multichannel Convolutional Decoding Network for Graph Classification.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3266243
Abstract: Graph convolutional networks (GCNs) have shown superior performance on graph classification tasks, and their structure can be considered as an encoder-decoder pair. However, most existing methods lack the comprehensive consideration of global and local in… read more here.
Keywords: information; encoder decoder; decoder; graph ... See more keywords
Semi-Supervised Hierarchical Graph Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3203703
Abstract: Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world… read more here.
Keywords: network; semi supervised; graph; hierarchical graph ... See more keywords
Differentially Private Graph Neural Networks for Whole-Graph Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3228315
Abstract: Graph Neural Networks (GNNs) have established themselves as state-of-the-art for many machine learning applications such as the analysis of social and medical networks. Several among these datasets contain privacy-sensitive data. Machine learning with differential privacy… read more here.
Keywords: differentially private; graph; graph neural; private graph ... See more keywords
A deep graph convolutional neural network architecture for graph classification
Sign Up to like & getrecommendations! Published in 2023 at "PLOS ONE"
DOI: 10.1371/journal.pone.0279604
Abstract: Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more… read more here.
Keywords: neural network; graph convolutional; deep graph; graph ... See more keywords