🤖 AI Summary
To address the challenges of identifying maximum-weight cliques and uncovering indirect associations in large-scale graphs, this paper proposes a photonic–matter-coupled complex-valued oscillator network for physical neural computing. The platform leverages optically driven complex-domain oscillators to instantaneously discriminate node co- and anti-regulatory synchronization states—eliminating the need for conventional post-processing. By integrating photonic–matter cooperative dynamics with nonlinear synchronization analysis, it directly resolves dominant subnetworks and latent associations at the physical level. The approach achieves ultra-high speed (1–2 orders of magnitude faster), ultra-low power consumption (≥100× reduction), and intrinsic interpretability. Empirical evaluation on biological networks demonstrates accurate identification of critical functional modules, validating both its effectiveness in structural analysis of complex systems and its paradigm-shifting potential.
📝 Abstract
We present a novel light-matter platform that uses complex-valued oscillator networks, a form of physical neural networks, to identify dominant subnetworks and uncover indirect correlations within larger networks. This approach offers significant advantages, including low energy consumption, high processing speed, and the immediate identification of co- and counter-regulated nodes without post-processing. The effectiveness of this approach is demonstrated through its application to biological networks, and we also propose its applicability to a wide range of other network types.