# Generative Graph Convolutional Network for Growing Graphs

## TL;DR

This paper introduces a generative graph convolutional network capable of modeling the growth of graphs, effectively handling new isolated nodes by learning adaptive node representations within a unified framework.

## Contribution

It proposes a novel unified generative GCN that learns representations for all nodes, including isolated new nodes, using a variational approach with adaptive regularization.

## Key findings

- Outperforms existing methods on citation network benchmarks
- Effectively handles cold start problem for new nodes
- Demonstrates superior graph generation quality

## Abstract

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 approach on several benchmark citation network datasets.