关于网络嵌入和图卷积神经网络的一些思考（34页）——基于图神经网络认知的智能计算专场.pdf

1、关于网络嵌入和图卷积神经网络的一些思考崔 鹏清华大学Perspectives and Outlook on Network Embedding and GCN网络/图数据图是对于数据的一/通用、全面、复杂的表示形式网络无处不在社交网络生物网络金融网络物联网信息网络物流网络为什么网络很重要？我们很少只关心数据本身，而不关心数据之间的关联Reflected by relational subjects Decided by relational subjects TargetTargetImage CharacterizationSocial Capital网络数据对机器学习模型不友好G=(V,E

2、)LinksTopologyInapplicability of ML methodsNetwork DataFeature ExtractionPattern DiscoveryPipeline for network analysisNetwork ApplicationsLearnabilityLearning from NetworksNetwork EmbeddingGNNG=(V,E)G=(V)Vector SpacegenerateembedEasy to parallelCan apply classical ML methods网络嵌入(Network Embedding)网

3、络嵌入的目标Goal Support network inference in vector spaceReflect network structureMaintain network propertiesBACTransitivitypBasic idea:recursive definition of statespA simple example:PageRank图神经网络GNNF.Scarselli,et al.The graph neural network model.IEEE TNN,2009.定义在图拓扑上的学习框架pMain idea:pass messages betwe

4、en pairs of nodes&agglomeratepStacking multiple layers like standard CNNs:pState-of-the-art results on node classification图卷积神经网络GCNT.N.Kipf and M.Welling.Semi-supervised classification with graph convolutional networks.ICLR,2017.图神经网络GNN简史网络嵌入与图神经网络GraphFeatureNetwork EmbeddingGCNInputTask resultsM

5、odelOutputEmbeddingTask resultsFeatureTopology to VectorFusion of Topology and FeaturesUnsupervised vs.(Semi-)Supervised图卷积网络 v.网络嵌入p In some sense,they are different.p Graphs exist in mathematics.(Data Structure)p Mathematical structures used to model pairwise relations between objectsp Networks ex

6、ist in the real world.(Data)p Social networks,logistic networks,biology networks,transactionnetworks,etc.p A network can be represented by a graph.p A dataset that is not a network can also be represented by a graph.图卷积网络应用于自然语言处理pMany papers on BERT+GNN.pBERT is for retrieval.pIt creates an initial