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...
By adjusting the value of p , the p -Laplacian based regularizer restricts the solution space of graph framelet into the desirable region based on the graph homophilic features. We propose an algorithm to effectively solve a more generalized regularization problem and prove that the algorithm ...
self-training, consistency regularization, co-training, transductive support vector machines, and graph-based methods. And with the advent of deep learning, the majority of these methods were adapted and intergrated into existing deep learning frameworks to take advantage of unlabled data. ...
On the basis of TRMF, we propose a novel LSTM and Graph Laplacian regularized matrix factorization (LSTM-GL-ReMF). In LSTM-GL-ReMF, its temporal regularizer depends on the state-of-the-art Long Short-term Memory (LSTM) model, and the spatial regularizer is designed based on Graph Laplacian...
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...
(1)Here,L0denotesthesupervisedlossw.r.t.thelabeledpartofthegraph,f(·)canbeaneuralnetwork-likedifferentiablefunction,λisaweighingfactorandXisamatrixofnodefeaturevectorsXi.∆=D−AdenotestheunnormalizedgraphLaplacianofanundirectedgraphG=(V,E)withNnodesvi∈V,edges(vi,vj)∈E,anadjacencymatrixA∈RN...
[2019].Apopularlearningparadigmisgraph-based/hypergraph-basedsemi-supervisedlearning(SSL)wherethegoalistoassignlabelstoinitiallyunlabelledverticesinagraph/hypergraphChapelleetal.[2010],Zhuetal.[2009],SubramanyaandTalukdar[2014].WhilemanytechniqueshaveusedexplicitLaplacianregularisationintheobjectiveZhouetal.[2003],...
We empiricallydemonstrate the performance advantages of CentSmoothie insimulations as well as real datasets.Index Terms—hypergraph neural networks, hypergraphLaplacian, smoothing, drug-drug interactionsI. I NTRODUCTIONIn drug-druginteractions (DDI), concurrentuse of two drugscan lead to side effects, ...
内容提示: How Curvature Enhance the Adaptation Power ofFramelet GCNsDai Shi ∗ , Yi Guo, Zhiqi Shao † , Junbin GaoAbstractGraph neural network (GNN) has been demonstrated powerful in modeling graph-structureddata. However, despite many successful cases of applying GNNs to various graph ...
Moreover, after simplifying and deducing the formula of the one-order spectral graph p-Laplacian convolution, we introduce a new layer-wise propagation rule based on the one-order approximation. Extensive experiment results on the Citeseer, Cora and Pubmed database demonstrate that our GpLCN ...