Learned Attention or Mechanical Flattening? Diagnosing Index Gains in Spillover Networks

This study demonstrates that while adding a graph attention layer to a spillover network model increases the total connectedness index, this gain is likely a mechanical artifact of variance redistribution rather than a genuine improvement in learned attention, as a simple shrinkage towards a uniform matrix achieves identical results with lower error.

Original authors: Ozan Nadirgil