  Photonic neural networks perform brain-inspired computations using photons
instead of electrons that can achieve substantially improved computing
performance. However, existing architectures can only handle data with regular
structures, e.g., images or videos, but fail to generalize to graph-structured
data beyond Euclidean space, e.g., social networks or document co-citation
networks. Here, we propose an all-optical graph representation learning
architecture, termed diffractive graph neural network (DGNN), based on the
integrated diffractive photonic computing units (DPUs) to address this
limitation. Specifically, DGNN optically encodes node attributes into strip
optical waveguides, which are transformed by DPUs and aggregated by on-chip
optical couplers to extract their feature representations. Each DPU comprises
successive passive layers of metalines to modulate the electromagnetic optical
field via diffraction, where the metaline structures are learnable parameters
shared across graph nodes. DGNN captures complex dependencies among the node
neighborhoods and eliminates the nonlinear transition functions during the
light-speed optical message passing over graph structures. We demonstrate the
use of DGNN extracted features for node and graph-level classification tasks
with benchmark databases and achieve superior performance. Our work opens up a
new direction for designing application-specific integrated photonic circuits
for high-efficiency processing of large-scale graph data structures using deep
learning.
