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Semi-Supervised Hierarchical Graph Classification
[article]
2022
arXiv
pre-print
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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 entity, e.g., a user in a social network, or a document in a document citation network. ...
Two graph learning problems have received a lot of attention recently, i.e., node classification and graph classification. ...
arXiv:2206.05416v1
fatcat:rsxjo4ztyng7lkh4nz6cozrmxm
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective
[article]
2023
arXiv
pre-print
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Recently, graph-based models designed for downstream tasks have significantly advanced research on graph neural networks (GNNs). ...
Moreover, given this framework, more accurate approximations of FPE are brought, guiding us to design a more powerful GNN: coupling graph neural network (CoGNet). ...
For a Markov random field with graph G, computing the posterior p is a computationally intractable task. ...
arXiv:2301.10536v1
fatcat:bnmxx34oezhqnfepi4dr4jleku
Rethinking Node-wise Propagation for Large-scale Graph Learning
[article]
2024
arXiv
pre-print
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Specifically, ATP has proven to be efficient in improving the performance of prevalent scalable GNNs for semi-supervised node classification while addressing redundant computational costs. ...
Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications ...
To the best of our knowledge, this work is the first to address the adverse impact of intricate topology in web-scale graph mining applications on the semisupervised node classification paradigm, providing ...
arXiv:2402.06128v1
fatcat:cjyhtuk6png5bllzrnmpn7lixq
Simple yet Effective Gradient-Free Graph Convolutional Networks
[article]
2023
arXiv
pre-print
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Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning. ...
Compared with nonlinear Graph Neural Network (GNN) models, linearized GNNs are much more time-efficient and can achieve comparable performances on typical downstream tasks such as node classification. ...
Gradient-free GCNs In this section, we elaborate on how to implement the gradient-free framework with the three linearized GCNs (SGC, SSGC and DGC) as its graph filters for the semisupervised node classification ...
arXiv:2302.00371v1
fatcat:j3yz32cnjrf4ln3r62gksfuai4
Learning Coordination Classifiers
2005
International Joint Conference on Artificial Intelligence
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by propagating beliefs on a graph over the data. ...
We present a new approach to ensemble classification that requires learning only a single base classifier. ...
Acknowledgments Research supported by the Alberta Ingenuity Centre for Machine Learning, NSERC, MITACS, and the Canada Research Chairs program. ...
dblp:conf/ijcai/GuoGS05
fatcat:pxcq3x7vrzhtbkpdhijyepagfq
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
[article]
2020
arXiv
pre-print
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Graph Neural Networks (GNNs) are the predominant technique for learning over graphs. ...
Here, we show that for many standard transductive node classification benchmarks, we can exceed or match the performance of state-of-the-art GNNs by combining shallow models that ignore the graph structure ...
In addition, we'd like to thank Matthias Fey and Marc Brockschmidt for insightful discussions. ...
arXiv:2010.13993v2
fatcat:7tnvv2aa6rb3jewabamr4ca6pm
Local Augmentation for Graph Neural Networks
[article]
2022
arXiv
pre-print
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Graph Neural Networks (GNNs) have achieved remarkable performance on graph-based tasks. ...
However, it remains an open question whether the neighborhood information is adequately aggregated for learning representations of nodes with few neighbors. ...
Specially, Songtao Liu is also thankful for the encouragement from Hao Yin. ...
arXiv:2109.03856v4
fatcat:6gtpa54jj5dsbg2mopndxfesq4
2021 Index IEEE Transactions on Cybernetics Vol. 51
2021
IEEE Transactions on Cybernetics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
Semisupervised Hyper-spectral Image Classification. ...
., +, TCYB Jan. 2021 52-63 Learning Dual Geometric Low-Rank Structure for Semisupervised Hyperspectral Image Classification. ...
doi:10.1109/tcyb.2021.3139447
fatcat:myjx3olwvfcfpgnwvbuujwzyoi
Convergence of the Graph Allen–Cahn Scheme
2017
Journal of statistical physics
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The graph Laplacian and the graph cut problem are closely related to Markov random fields, and have many applications in clustering and image segmentation. ...
This work analyzes the conditions for the graph diffuse interface algorithm to converge. ...
This paper studies the discrete graph Allen-Cahn scheme in [3] used for graph semisupervised classification. ...
doi:10.1007/s10955-017-1772-4
fatcat:72j5kdu4mncmdb3whgf3xlgfra
Robust Network Topology Inference and Processing of Graph Signals
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2302.13325v1 fatcat:qiagayonpbarjbak47mettfh2uOther Versions
the underlying support via a graph and (ii) exploit the topology of this graph to process the signals at hand. ...
signals, the graph support, and both. ...
in graph signal denoising and node classification problems. ...
arXiv:2302.13325v3
fatcat:fiwbj4t7kbfzbp2tfirw2muaim
Network anomaly detection with the restricted Boltzmann machine
2013
Neurocomputing
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Thus, a desirable characteristic of an effective model for network anomaly detection is its ability to adapt to change and to generalize its behavior to multiple different network environments. ...
with good classification accuracy capabilities to infer part of its knowledge from incomplete training data. ...
In theory, each parameter update in the learning process would require waiting for convergence of one Markov chain. ...
doi:10.1016/j.neucom.2012.11.050
fatcat:nwctdejjsndz7co54pls6tn7by
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
[article]
2023
arXiv
pre-print
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There are various reasons for the absence of sufficient data, ranging from time and cost constraints to ethical considerations. ...
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models. ...
The authors would like to thank the consortium for the successful cooperation. ✦ ...
arXiv:2205.04712v3
fatcat:saeka57uozelzowc4ql6uoe64a
2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25
2021
IEEE journal of biomedical and health informatics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
Landry, C., +, JBHI July 2021 2510-2520 Attention-Aware Residual Network Based Manifold Learning for White Blood Cells Classification. ...
Cui, K., JBHI Aug. 2021 3052-3060 HDC-Net: Hierarchical Decoupled Convolution Network for Brain Tumor Segmentation. ...
doi:10.1109/jbhi.2022.3140980
fatcat:ufig7b54gfftnj3mocspoqbzq4
Probabilistic Decoupling of Labels in Classification
[article]
2020
arXiv
pre-print
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In this paper we develop a principled, probabilistic, unified approach to non-standard classification tasks, such as semi-supervised, positive-unlabelled, multi-positive-unlabelled and noisy-label learning ...
For computing edges for the graph in SETRED we use the internal representation of the images, by the classifying neural network. ...
Markov processes. ...
arXiv:2006.09046v1
fatcat:m6bhvxgksfeobkb7exymhipvri
2020 Index IEEE Transactions on Industrial Informatics Vol. 16
2020
IEEE Transactions on Industrial Informatics
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Forests-Based Model for Ultra-Short-Term Prediction of PV Characteristics; TII Jan. 2020 202-214 Imran, A., see Hussain, B., TII Aug. 2020 4986-4996 Imran, M., see Fu, S., TII Sept. 2020 6013-6022 ...
Cai, H., TII Jan. 2020 587-594 Jiang, L., see Xia, Z., TII Jan. 2020 629-638 Jiang, Q., Yan, S., Yan, X., Yi, H., and Gao, F., Data-Driven Two-Dimensional Deep Correlated Representation Learning for ...
., +, TII Sept. 2020 5780-5791 Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification. ...
doi:10.1109/tii.2021.3053362
fatcat:blfvdtsc3fdstnk6qoaazskd3i
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