1、網絡嵌入和圖卷積神經網絡技術概述技術創新 變革未來網絡/圖數據圖是對于數據的一/通用、全面、復雜的表示形式網絡無處不在社交網絡生物網絡金融網絡物聯網信息網絡物流網絡為什么網絡很重要？我們很少只關心數據本身，而不關心數據之間的關聯Reflected by relational subjectsDecided by relational subjectsTargetImage CharacterizationTargetSocial Capital網絡數據對機器學習模型 不友好G = ( V, E )LinksTopologyInapplicability of ML methodsNetwork

2、 DataFeature ExtractionPattern DiscoveryPipeline for network analysisNetwork ApplicationsLearnabilityLearning from NetworksNetwork EmbeddingGNNG = ( V, E )G = ( V )Vector SpacegenerateembedEasy to parallelCan apply classical ML methods網絡嵌入 (Network Embedding)網絡嵌入的目標GoalSupport network inference in v

3、ector spaceReflect network structureMaintain network propertiesBACTransitivitypBasic idea: recursive definition of statespA simple example: PageRank圖神經網絡GNNF. Scarselli, et al. The graph neural network model. IEEE TNN, 2009.定義在圖拓撲上的學習框架pMain idea: pass messages between pairs of nodes & agglomeratepS

4、tacking multiple layers like standard CNNs:pState-of-the-art results on node classification圖卷積神經網絡GCNT. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. ICLR, 2017.圖神經網絡GNN簡史網絡嵌入與圖神經網絡GraphFeatureNetwork EmbeddingInputModelOutputEmbeddingTask resultsFeatureTo

5、pology to VectorGCNTask resultsFusion of Topology and FeaturesUnsupervised vs. (Semi-)Supervised圖卷積網絡 v . 網絡嵌入pIn some sense, they are different.pGraphs exist in mathematics. (Data Structure)p Mathematical structures used to model pairwise relations between objectspNetworks exist in the real world.

6、(Data)p Social networks, logistic networks, biology networks, transaction networks, etc.pA network can be represented by a graph.pA dataset that is not a network can also be represented by a graph.圖卷積網絡應用于自然語言處理p Many papers on BERT + GNN.p BERT is for retrieval.pIt creates an initial graph of relev

7、ant entities and the initial evidence.p GNN is for reasoning.pIt collects evidence (i.e., old messages on the entities) and arrive at new conclusions (i.e., new messages on the entities), by passing the messages around and aggregating them.Cognitive Graph for Multi-Hop Reading Comprehension at Scale

8、. Ding et al., ACL 2019.Dynamically Fused Graph Network for Multi-hop Reasoning. Xiao et al., ACL 2019.圖卷積網絡應用于計算機視覺pA popular trend in CV is to construct a graph during the learning process.pTo process multiple objects or parts in a scene, and to infer their relationships.pExample: Scene graphs.Sce

9、ne Graph Generation by Iterative Message Passing. Xu et al., CVPR 2017.Image Generation from Scene Graphs. Johnson et al., CVPR 2018.圖卷積網絡應用于符號推理pWe can view the process of symbolic reasoning as a directed acyclic graph.pMany recent efforts use GNNs to perform symbolic reasoning.Learning by Abstract

10、ion: The Neural State Machine. Hudson & Manning, 2019.Can Graph Neural Networks Help Logic Reasoning? Zhang et al., 2019.Symbolic Graph Reasoning Meets Convolutions. Liang et al., NeurIPS 2018.pStructural equation modeling, a form of causal modeling, tries to describe the relationships between the v

11、ariables as a directed acyclic graph (DAG).pGNN can be used to represent a nonlinear structural equation and help find the DAG, after treating the adjacency matrix as parameters.圖卷積網絡應用于結構方程建模DAG-GNN: DAG Structure Learning with Graph Neural Networks. Yu et al., ICML 2019.（大多數）圖卷積網絡方法的Pipeline Co-oc

12、currence (neighborhood)網絡嵌 : 拓撲向量化 High-order proximities網絡嵌 : 拓撲向量化 Communities網絡嵌 : 拓撲向量化 Heterogeneous networks網絡嵌 : 拓撲向量化（大多數）網絡嵌入方法的PipelineLearning for Networks v.s. Learning via GraphsLearning for networksLearning Via GraphsNetwork EmbeddingGCN網絡嵌入方法解決的核心問題Node NeighborhoodCommunityPair-wise

13、ProximityHyper EdgesGlobal StructureReducing representation dimensionality while preserving necessary topologicalstructures and properties.Nodes & LinksNon-transitivityAsymmetric TransitivityDynamicUncertaintyHeterogeneityInterpretabilityTopology-driven圖卷積神經網絡方法解決的核心問題Fusing topologyand featuresin t

14、he wayof smoothing features with the assistance of topology.Feature-driven如果問題是拓撲驅動的？Since GCN is filtering features, it is inevitably feature-drivenStructure only provides auxiliary information (e.g. for filtering/smoothing)When feature plays the key role, GNN performs good How about the contrary?S

15、ynthesis data: stochastic block model + random featuresMethodResultsRandom10.0GCN18.31.1DeepWalk99.00.1網絡嵌入 vs 圖神經網絡There is no better one, but there is more proper one.反思：圖神經網絡是否真的是深度學習方法？ This simplified GNN (SGC) showsremarkable results:Node classificationText Classification反思：圖神經網絡是否真的是深度學習方法？Wu, Felix, et al. S