Ironing the Graphs: Toward a Correct Geometric Analysis of Large-Scale Graphs
<|reference_start|>Ironing the Graphs: Toward a Correct Geometric Analysis of Large-Scale Graphs: Graph embedding approaches attempt to project graphs into geometric entities, i.e, manifolds. The idea is that the geometric properties of the projected manifolds are helpful in the inference of graph properties. However, if the choice of the embedding manifold is incorrectly performed, it can lead to incorrect geometric inference. In this paper, we argue that the classical embedding techniques cannot lead to correct geometric interpretation as they miss the curvature at each point, of manifold. We advocate that for doing correct geometric interpretation the embedding of graph should be done over regular constant curvature manifolds. To this end, we present an embedding approach, the discrete Ricci flow graph embedding (dRfge) based on the discrete Ricci flow that adapts the distance between nodes in a graph so that the graph can be embedded onto a constant curvature manifold that is homogeneous and isotropic, i.e., all directions are equivalent and distances comparable, resulting in correct geometric interpretations. A major contribution of this paper is that for the first time, we prove the convergence of discrete Ricci flow to a constant curvature and stable distance metrics over the edges. A drawback of using the discrete Ricci flow is the high computational complexity that prevented its usage in large-scale graph analysis. Another contribution of this paper is a new algorithmic solution that makes it feasible to calculate the Ricci flow for graphs of up to 50k nodes, and beyond. The intuitions behind the discrete Ricci flow make it possible to obtain new insights into the structure of large-scale graphs. We demonstrate this through a case study on analyzing the internet connectivity structure between countries at the BGP level.<|reference_end|>
@article{naama2024ironing,
    title={Ironing the Graphs: Toward a Correct Geometric Analysis of Large-Scale
  Graphs},
    author={Saloua Naama, Kav'e Salamatian, Francesco Bronzino},
    journal={arXiv preprint arXiv:2407.21609},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21609},
    primaryClass={cs.CG cs.LG}
}