为了解决上述问题,作者提出了一种新的基于 p-Laplacian 的 GNN 模型,称为 pGNN,其消息传递机制源自离散正则化框架,理论上可以解释为在谱域上定义的多项式图滤波器的近似值 p-拉普拉斯算子。 p-Laplacian 消息传递的频谱分析表明,它可以用作低高通滤波器,因此pGNN使 GNN 能够处理异构图和具有非信息拓扑的图。代码可在 gi
p-Laplacian regularizationGraph neural networks (GNNs) have achieved remarkable results for various graph learning tasks. However, one of the recent challenges for GNNs is to adapt to different types of graph inputs, such as heterophilic graph datasets in which linked nodes are more likely to ...
The graph convolutional networks (GCN) generalizes convolution neural networks into the graph with an arbitrary topology structure. Since the geodesic function in the null space of the graph Laplacian matrix is constant, graph Laplacian fails to preserve the local topology structure information between ...
graph-based semi-supervised learning and present some new results on the consistency ofp-Laplacian semi-supervised learning using the stochastic tug-of-war game interpretation of thep-Laplacian. We also present the results of some numerical experiments that illustrate our results and suggest directions ...
This is the code repository for paper 'Real-time Spatiotemporal Prediction and Imputation of Traffic Status Based onLSTM and Graph Laplacian Regularized Matrix Factorization' which is submitted to Transportation Research Part C: Emerging Technologies ...
To solve the above limitations, we propose a Power Fault Retrieval and Recommendation Model (PF2RM) based on the knowledge graph. Combined with the power fault knowledge graph, this model designs a user-polymorphic recommendation method for both the cold-start mode and general retrieval mode. Spe...
2006;Westonetal.,2012),e.g.byusingagraphLaplacianregularizationterminthelossfunction: L=L 0 +λL reg ,withL reg = i,j A ij f(X i )−f(X j ) 2 =f(X) ∆f(X).(1) Here,L 0 denotesthesupervisedlossw.r.t.thelabeledpartofthegraph,f(·)canbeaneuralnetwork- likedifferentiablefu...
,weproposemoti-basedgraphattentionmodel,calledMotiConvolutionalNetworks,whichgeneralizespastapproachesusingweightedmulti-hopmotiadjacencymatricescapturehigher-orderneighborhoods.Anovelattentionmechanismisusedalloweachindividualnodemostrelevantneighborhoodapplyitsflter.Weevaluateourapproachgraphsromdierentdomains(socialnetworks...
10.5.4 Example of Laplacian Support Vector Machine 494 10.5.5 Example of Graph-Based Label Propagation 498 10.5.6 Examples of Multiple Kernel Learning 498 10.6 Concluding Remarks 501 10.7 Questions and Problems 502 11 Clustering and Anomaly Detection with Kernels 503 ...
For hypergraph clustering, various methods have been proposed to define hypergraph p-Laplacians in the literature. This work proposes a general framework f