Discrete Relational Network Dynamics (DRND): A Fully Emergent Framework for Spacetime, Matter, and the Evolution of Fundamental Constants

Contemporary cosmology exhibits persistent internal tensions, including discrepancies in late- and early-Universe inferences of the Hubble parameter and open questions surrounding the physical origin of the Dark Sector. Most extensions of the concordance model modify the stress-energy content while retaining a smooth background manifold and fixed fundamental constants. Here we outline Discrete Relational Network Dynamics (DRND), a background-independent framework in which spacetime, matter degrees of freedom, and effective dynamical laws emerge from an evolving discrete graph of causal relations. Dimensionality is treated as a dynamical, scale- and environment-dependent property characterized by the network's spectral dimension dS defined via diffusion on the graph. Within a minimal toy description based on random-walk transport, we propose that the effective signal-propagation speed cₑff can depend on the network topology, with high-connectivity phases (large dS) enabling faster causal processing and low-connectivity/percolative regimes (reduced dS) inducing longer traversal times. We discuss a phenomenological parameterization in which cₑff (z) modifies cosmological distance measures and can mimic part of the phenomenology usually attributed to Dark Energy under the assumption of constant c. We also formulate a qualitative screening picture in which virialized regions remain effectively three-dimensional (dS ≈ 3), suppressing local detectability. The paper is intended as a framework and a set of falsifiable directions, and we explicitly separate heuristic scaling relations from future work required for full dynamical consistency and data fitting (SNe, BAO, CMB).