Graph Learning with Distributional Edge Layouts
Xinjian Zhao Chaolong Ying Tianshu Yu
Graph Neural Networks (GNNs) learn from graph-structured data by passing
local messages between neighboring nodes along edges on certain topological
layouts. Typically, these topological layouts in modern GNNs are
deterministically computed (e.g., attention-based GNNs) or locally sampled
(e.g., GraphSage) under heuristic assumptions. In this paper, we for the first
time pose that these layouts can be globally sampled via Langevin dynamics
following Boltzmann distribution equipped with explicit physical energy,
leading to higher feasibility in the physical world. We argue that such a
collection of sampled/optimized layouts can capture the wide energy
distribution and bring extra expressivity on top of WL-test, therefore easing
downstream tasks. As such, we propose Distributional Edge Layouts (DELs) to
serve as a complement to a variety of GNNs. DEL is a pre-processing strategy
independent of subsequent GNN variants, thus being highly flexible.
Experimental results demonstrate that DELs consistently and substantially
improve a series of GNN baselines, achieving state-of-the-art performance on
multiple datasets.