杨亮-From Propagation to Ego-Network Modeling.pdf

1、From Propagation to Ego-Network Modeling in GNNsLiang YangOutline Existing Graph Neural Networks Orthogonal Propagation with Ego-Network Modeling Graph Neural Networks without Propagations Self-supervised GNNs via Low-Rank Decomposition ConclusionsOPEN:Orthogonal Propagation with Ego-Network Modelin

2、g123456Original GraphxXCLearnableMulti-channelsDimension Reduction Perspectivex=Mapping FunctionData PointMapping via Whole Ego-NetworkOrthogonalu1u2Whole Ego-networku1u2356Ego-network ExtractionCx=XHMotivation:Irrelevant propagationsThe propagations to each node are Irrelevant.10Pairwise LearningIn

3、tra-channel Irrelevant=x+xxPropagation WeightNode Embedding356Motivation:Irrelevant propagationsChannel-1,j=1Green lineChannel-2,j=2Orange lineInter-channel IrrelevantThe propagations in multi-channels are Irrelevant.35611356356Motivation:Irrelevant propagationsMulti-channelsThe propagations in mult

4、i-channels are Irrelevant.The propagations to each node are Irrelevant.123456C356xEgo-network Extraction=XH=xxx+Propagation PerspectivePropagationWeightNode EmbeddingxXCLearnableOriginal GraphPairwise LearningInter-channel IrrelevantIntra-channel IrrelevantExisting GNNs with Irrelevant Propagations1

5、2Irrelevant Relevant :the F-dimensional representations of one node Node Embedding Number of Nodes:Propagation Weights :the 1-dimensional representations of F Data Points Data Point Number of Data Points:F:Mapping Functions=xxx+PropagationWeightNode EmbeddingCx=XHPropagation PerspectiveDimension Red

6、uction PerspectiveNode3Node5Node6XXData Point1356x=Mapping FunctionData Point13Data Point2Data Point3Data Point4OPEN:Orthogonal Propagation with Ego-Network modelingMapping via Whole Ego-NetworkOrthogonalInter-channel Relevantu1u2Whole Ego-networkIntra-channel Relevantu1u2Dimension Reduction Perspec