Feature Propagation on Graph: A New Perspective to Graph Representation Learning
This work addresses a theoretical gap in graph learning for researchers and practitioners, though it is incremental as it builds on existing methods.
The paper tackles the lack of convergence guarantees in feature propagation for graph representation learning, which can cause numerical issues and task failures, and it demonstrates competitive performance in fraud detection applications.
We study feature propagation on graph, an inference process involved in graph representation learning tasks. It's to spread the features over the whole graph to the $t$-th orders, thus to expand the end's features. The process has been successfully adopted in graph embedding or graph neural networks, however few works studied the convergence of feature propagation. Without convergence guarantees, it may lead to unexpected numerical overflows and task failures. In this paper, we first define the concept of feature propagation on graph formally, and then study its convergence conditions to equilibrium states. We further link feature propagation to several established approaches such as node2vec and structure2vec. In the end of this paper, we extend existing approaches from represent nodes to edges (edge2vec) and demonstrate its applications on fraud transaction detection in real world scenario. Experiments show that it is quite competitive.