Vertex-transitive strongly regular graphs in the switching class of doubly transitive two-graphs

What the math gives to ML

The paper supplies highly symmetric sparse graphs obtained from switching classes of regular two-graphs, with exact strongly regular parameters controlling every pairwise common-neighbor count. This uniformity is transferable to sparse neural connectivity: unlike an arbitrary k-regular mask, the resulting interaction graph is vertex-transitive and has only two possible two-hop overlap patterns, which can improve information mixing without hubs. The most direct experiment is to replace dense self-attention or MLP token mixing by a fixed strongly regular graph mask while preserving residual and local feature projections, and compare against random regular and expander masks at equal edge count.