Growing Regression Forests by Classification: Applications to Object Pose Estimation
, 2014 In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules
Chellappa, Rama, Hara, Kota
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Node Classification of Imbalanced Data Using Ensemble Graph Neural Networks
Applied SciencesIn real-world scenarios, many datasets suffer from class imbalance. For example, on online review platforms, the proportion of fake and genuine comments is often highly skewed.
Yuan Liang
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Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder
IEEE Access, 2022 Network embedding plays a critical role in many applications. Node classification, link prediction, and network visualization are examples of such applications.
Amr Thabit Al-Furas +3 more
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Classifying Network Data with Deep Kernel Machines [PDF]
, 2010 Inspired by a growing interest in analyzing network data, we study the problem of node classification on graphs, focusing on approaches based on kernel machines. Conventionally, kernel machines are linear classifiers in the implicit feature space.
Tang, Xiao, Zhu, Mu
core
Transfer Learning across Networks for Collective Classification
, 2013 This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances ...
Fang, Meng, Yin, Jie, Zhu, Xingquan
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GRE2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning
IEEE AccessGraph representation learning aims to preserve graph topology when mapping nodes to vector representations, enabling downstream tasks like node classification and community detection.
Quanjun Li +6 more
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Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach
EntropyThe identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization.
Mateusz Stolarski +2 more
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Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]
Jisuanji kexueNode classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
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Integrated Node Encoder for Labelled Textual Networks
, 2020 Voluminous works have been implemented to exploit content-enhanced network embedding models, with little focus on the labelled information of nodes. Although TriDNR leverages node labels by treating them as node attributes, it fails to enrich unlabelled ...
Ma, Ye, Zong, Lu
core
A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification
CoRR, 2020 Node embedding methods find latent lower-dimensional representations which are used as features in machine learning models. In the last few years, these methods have become extremely popular as a replacement for manual feature engineering. Since authors use various approaches for the evaluation of node embedding methods, existing studies can rarely be ...
Christoph Martin, Meike Riebeling
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