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Semi-supervised Learning Based on Joint Diffusion of Graph Functions and Laplacians
[chapter]
2016
Lecture Notes in Computer Science
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We observe the distances between estimated function outputs on data points to create an anisotropic graph Laplacian which, through an iterative process, can itself be regularized. ...
Our algorithm is instantiated as a discrete regularizer on a graph's diffusivity operator. ...
an anisotropic diffusion process with a new metric on M [2] . ...
doi:10.1007/978-3-319-46454-1_43
fatcat:qtzgd247ojdbziwhjvnkah3s4y
Metadata-conscious anonymous messaging
2016
IEEE Transactions on Signal and Information Processing over Networks
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The spread of messages on these platforms can be modeled by a diffusion process over a graph. ...
We further demonstrate empirically that adaptive diffusion hides the source effectively on real social networks. ...
ACKNOWLEDGMENTS The authors thank the anonymous reviewers for their helpful comments and Paul Cuff for helpful discussions and for pointing out the Bayesian interpretation of the Polya's urn process. ...
doi:10.1109/tsipn.2016.2605761
fatcat:rugyvh2ojfb3xa7jrnddmzehy4
Geometric PDEs on Weighted Graphs for Semi-supervised Classification
2014
2014 13th International Conference on Machine Learning and Applications
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In this paper, we consider the adaptation of two Partial Differential Equations (PDEs) on weighted graphs, p-Laplacian and eikonal equations, for semi-supervised classification tasks. ...
While the p-Laplacian on graphs was intensively used in data classification, few works relate to the eikonal equation for data classification. ...
for label diffusion over a graph using regularization [8] . ...
doi:10.1109/icmla.2014.43
dblp:conf/icmla/ToutainEL14
fatcat:btihul5v4rfnnjz65xeax6vgnq
Adaptive graph filtering: Multiresolution classification on graphs
2013
2013 IEEE Global Conference on Signal and Information Processing
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Adaptive graph filters combine decisions from multiple graph filters using a weighting function that is optimized in a semi-supervised manner. ...
We also demonstrate the multiresolution property of adaptive graph filters by connecting them to the diffusion wavelets. ...
graph filter coincides with the diffusion function. ...
doi:10.1109/globalsip.2013.6736906
dblp:conf/globalsip/ChenSMK13
fatcat:k5h4tezxgja5lk2tffotxfr5fy
Nonlocal graph regularization for image colorization
2008
Pattern Recognition (ICPR), Proceedings of the International Conference on
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Image colorization is then considered as a graph regularization problem for a function mapping vertices to chrominances. ...
Then, p-Laplace regularization on weighted graphs problem is presented and the associated filter family. ...
Construction of graphs The minimization problem (2) , and the discrete diffusion processes (6) , can be used to regularize any function defined on a finite set V of discrete data. ...
doi:10.1109/icpr.2008.4761617
dblp:conf/icpr/LezorayTE08a
fatcat:4zjfxc3qvjes7cx5xk4axu3cym
Domain adaptive semantic diffusion for large scale context-based video annotation
2009
2009 IEEE 12th International Conference on Computer Vision
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In addition, the proposed approach is very efficient, completing DASD over 374 concepts within just 2 milliseconds for each video shot on a regular PC. ...
Starting with a large set of concept detectors, the proposed DASD refines the initial annotation results using graph diffusion technique, which preserves the consistency and smoothness of the annotation ...
From Equation 2, the gradient of with respect to on the semantic graph is: (7) Thus we can derive the following iterative diffusion process (8) Through exponentiating the graph Laplacian with step size ...
doi:10.1109/iccv.2009.5459295
dblp:conf/iccv/JiangWCN09
fatcat:vrhzwcuggzeqtcgfpixqpvbhpe
A scale-space based hierarchical representation of discrete data
2011
2011 18th IEEE International Conference on Image Processing
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A new hierarchical representation of general discrete data sets living on graphs is proposed. The approach takes advantage of recent works on graph regularization. ...
Moreover, the approach is particularly well adapted to the processing of digital images, where nonlocal processing allows to better handle repetitive patterns usually present in natural images. ...
Bougleux, "Nonlocal discrete regularization on weighted graphs: A framework for image and manifold processing," IEEE Transactions on Image Processing, vol. 17, no. 7, 2008. ...
doi:10.1109/icip.2011.6116144
dblp:conf/icip/HidaneLE11
fatcat:aioan5mvkjhsrnygqxbml7kv24
APRP: An Anonymous Propagation Method in Bitcoin Network
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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delay factor and constantly adjusts PR-value of nodes to adapt to network dynamics. ...
Experiments on both simulated and real Bitcoin networks confirm the superiority of APRP in terms of 20-50% performance enhancement under various deanonymization attacks. ...
APRP(Trickle) estimation on d-regular trees. θ = 1. (c) Diffusion vs. APRP(Diffusion) estimation on dregular trees. θ = 1. (d) Diffusion vs. APRP(Diffusion) estimation on 4-regular trees. ...
doi:10.1609/aaai.v33i01.330110073
fatcat:nc5mjkjhyrdrlbr4jvdmgzve5m
Image Processing with Nonlocal Spectral Bases
2008
Multiscale Modeling & simulation
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This framework also allows one to define thresholding operators in adapted orthogonal bases. These bases are eigenvectors of the discrete Laplacian on a manifold adapted to the geometry of the image. ...
All these schemes lead to regularizations that exploit the manifold structure of the lifted image. ...
The geometric diffusion framework of Coifman et al. [16] can be used to process functions defined on a graph computed from M. Szlam et al. ...
doi:10.1137/07068881x
fatcat:k7ti6526nvc7lpx4kisepctyoa
Graph Neural Networks With Lifting-based Adaptive Graph Wavelets
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2108.01660v2 fatcat:36bagk5z5nc6hodt6awyvzz3oyOther Versions
However, existing SGNNs are limited in implementing graph filters with rigid transforms (e.g., graph Fourier or predefined graph wavelet transforms) and cannot adapt to signals residing on graphs and tasks ...
In this paper, we propose a novel class of graph neural networks that realizes graph filters with adaptive graph wavelets. ...
GWNN [22] implements graph wavelet convolutions with diffusion wavelets [38] , where the wavelet coefficients are processed with parameter-intensive diagonal filters whose size depends on the number ...
arXiv:2108.01660v3
fatcat:liunq2ozw5dxlps7s3wcc52vry
Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation
2021
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
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distances on unsupervised graph domain adaptation tasks. ...
The use of the heat kernel on graphs has recently given rise to a family of so-called Diffusion-Wasserstein distances which resort to the Optimal Transport theory for comparing attributed graphs. ...
Domain Adaptation on synthetic data a) Synthetic data generation: The process for generating a synthetic graph of size N is the following. ...
doi:10.1109/ictai52525.2021.00125
fatcat:tfxm37hpjzcpjez7lzaj4mb7t4
Hiding the Rumor Source
[article]
2016
arXiv
pre-print
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Experiments on a sampled Facebook network demonstrate that adaptive diffusion effectively hides the location of the source even when the graph is finite, irregular and has cycles. ...
We introduce a novel messaging protocol, which we call adaptive diffusion, and show that under the snapshot adversarial model, adaptive diffusion spreads content fast and achieves perfect obfuscation of ...
Adaptive diffusion achieves the fundamental limit of Figure 18 ) on d-regular trees. ...
arXiv:1509.02849v2
fatcat:f4q6qv4nrnhifdy7yg7irii4bm
Neighborhood Adaptive Graph Convolutional Network for Node Classification
2019
IEEE Access
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Besides, one learnable feature refinement process is used in the model to obtain high-level node representations with sufficient expressive power. ...
Particularly, we construct a convolutional kernel abstracted from the diffusion process, named as the neighborhood adaptive kernel to more precisely learn and integrate related neighborhood node information ...
To avoid over-fitting, we also use L2 regularization term on the loss function with coefficient 5 × 10 −4 and dropout mechanism with a dropout rate of 0.5. ...
doi:10.1109/access.2019.2955487
fatcat:lf5fyvge7za5bofhxwq4zvimcu
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting
[article]
2021
arXiv
pre-print
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Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution ...
Based on the homophily assumption of GNN, we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar ...
Specifically, the directionaware diffusion convolution function f * (X; G, Θ, D W ) aggregates the input X on graph G(V, E, A r ) with parameters Θ and D W in the following way: where M is the total diffusion ...
arXiv:2109.12846v1
fatcat:2dhl3lhehnf5pki53wbtkbltk4
Discrete Regularization for Perceptual Image Segmentation via Semi-Supervised Learning and Optimal Control
2007
Multimedia and Expo, 2007 IEEE International Conference on
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Furthermore, the spectral segmentation is penalized and adjusted using labeling prior and optimal window-based affinity functions in a regularization framework on discrete graph spaces. ...
In this approach, first, a spectral clustering method is embedded and extended into regularization on discrete graph spaces. ...
DISCRETE REGULARIZATION ON GRAPHS A general weighted undirected graph G = (V, E) consists of two sets A and B with edges E, i.e., edges with one endpoint in A and the other in B, where V = {v i } n i=1 ...
doi:10.1109/icme.2007.4285067
dblp:conf/icmcs/ZhengH07
fatcat:u46wpwjorraendk6gpparbb5ru
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