[1903.02640] Generative Graph Convolutional Network for Growing Graphs

Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existing graph. Despite the emerging literature in learning graph representation and graph generation, most of them can not handle isolated new nodes without nontrivial modifications. The challenge arises due to the fact that learning to generate representations for nodes in observed graph relies heavily on topological features, whereas for new nodes only node attributes are available. Here we propose a unified generative graph convolutional network that learns node representations for all nodes adaptively in a generative model framework, by sampling graph generation sequences constructed from observed graph data. We optimize over a variational lower bound that consists of a graph reconstruction term and an adaptive Kullback-Leibler divergence regularization term. We demonstrate the superior performance of our appro

Date: 2019/06/06 00:48

Related Entries

Read more [1907.06902] Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation...
0 users, 41 mentions 2019/07/22 23:16
Read more [1906.00091] Deep Learning Recommendation Model for Personalization and Recommendation Systems
5 users, 2 mentions 2019/09/13 00:50
Read more [1902.07243] Graph Neural Networks for Social Recommendation
0 users, 1 mentions 2019/08/31 17:17
Read more DLRM: An advanced, open source deep learning recommendation model
2 users, 65 mentions 2019/07/03 03:48
Read more GitHub - MaurizioFD/RecSys2019_DeepLearning_Evaluation: This is the repository of our article publis...
0 users, 2 mentions 2019/09/16 15:47