2024-1-27真实复杂场景下的图神经网络-天津大学何东晓.pdf

1、汇报人：何东晓汇报人：何东晓 天津大学天津大学 教授教授汇报时间：汇报时间：20232023年年1 1月月2727日日真实复杂场景下的图神经网络真实复杂场景下的图神经网络CatalogueAdversarial Representation Mechanism Learning for Network Embedding01Block Modeling-Guided Graph Convolutional Neural NetworksImproving Distinguishability of Class for Graph Neural NetworksContrastive Learn

2、ing Meets Homophily:Two Birds with One Stone020304Adversarial Representation Mechanism Learning for Network EmbeddingDongxiao He,et al.Adversarial Representation Mechanism Learning for Network Embedding,IEEE Transactions on Knowledge and Data Engineering(TKDE),2023,35(2):1200-1213.Introduction Graph

3、 representation learning:Graph representation learning aims to transform nodes on the graph into low-dimensionaldense vectors whilst still preserving the attribute features of nodes and structure features ofgraphs.表征学习图表征下游任务节点级别任务边级别任务图级别任务|d表征学习图表征下游任务节点级别任务边级别任务图级别任务34256798110=(,)|dIntroduction

4、Graph representation learning based on GCN:X=()1|()|Feature TransformationNeighborhood Aggregation12345678131313Neighborsof node 2(2)IntroductionGAN is inspired by the two-player game in game theory,which contains:A generator G(generating data that resemble real data).The generators goal is to foolt

5、he discriminator by generating data that are as similar to the real dataas possible.A discriminator D(distinguishing real data from generated data).The discriminators goalis to debunk the generator by discriminating between real data and generated data.GenerativeAdversarial Network(GAN):Introduction

6、ARGA:PreliminariesSymbolNotation=(,)an undirected,unweighted and attributed network =1,2,nodes=a set of edges ma set of node attribute=adjacency matrixThe objective of network embedding is to cast each of the nodes inthe network to a vector.Notations and the ProblemThe Approach-ArmGANAutoencoder wit