2-4 集成多关系图神经网络.pdf

1、Ensemble Multi-Relational Graph Neural NetworksYuling Wang|Yuling Wang12,Hao Xu2,Yanhua Yu1,Mengdi Zhang2,Zhenhao Li1,Yuji Yang2,and Wei Wu21Beijing University of Posts and Telecommunications 2Meituan01020304目录目录CONTENTA Unified View on Graph Neural NetworksRelational Graph Neural NetworksEnsemble M

2、ulti-Relational Graph Neural NetworksSummarization|01A Unified View on Graph Neural Networksl A Unified Optimization Frameworkl Design GNNs based on Optimization FrameworkA Unified View on Graph Neural Networks|Propagation Mechanisms K-layer propagation process A decoupled propagation process(e.g.AP

3、PNP DAGNN)(e.g.GCN)Combination operation Two essential information sources of existing GNNs1)Directly utilizes the K-th layer output2)Using outputs from other layers1)Network topology:homophily property2)Node features:low/high-frequency information|A Unified View on Graph Neural Networks|Optimizatio

4、n Framework Two common goals of existing GNNs1)Encode useful information from feature 2)Utilize the smoothing ability of topology Formulate as the following unified optimization objective=0 and =1GCN/SGC!|Formulate as the following unified optimization objectiveF1=F2=I,=1,=1/1,(0,1PPNP!p Closed Solu

5、tion p Iterative GradientAPPNP!A Unified View on Graph Neural Networks|Optimization Framework How to design new GNN models based on unified optimization framework?Design new%&or()*(1)Solve(1)New GNN models Examples 1 GNN with Low-pass Filtering Kernel p Closed Solution p Iterative Approximation buil

6、d the relationship of H and Z in both original and low-pass filtering spaces.A Unified View on Graph Neural Networks|Design Novel GNNs A macroscopic perspective on understanding GNNs Anew insight for designing novel GNNsOnly for homogeneous graphs！Examples 2 Elastic GNNlocal smoothnesssBuild new agg