Abstract:A graph is called Ricci-flat if its Ricci curvatures vanish on all edges, here the definition of Ricci curvature on graphs was given by Lin-Lu-Yau [5]. The authors in [4] and [2] obtained a complete characterization for all Ricci-flat graphs with girth at least five. In this paper, we completely determined all Ricci-flat graphs with maximum degree at most 4.
“…Then G is isomorphic to a finite path, a simple cycle, a prism graph, a Möbius ladder, a particular graph as shown in Figure 1, or a quasi-ladder graph as shown in Figure 2. Bai, Lu and Yau [BLY21] classified all Ricci-flat graphs with maximum degree at most 4. Compared with our results, they require a more stringent curvature condition but relax the maximum degree condition from 3 to 4, which causes a huge increase in the complexity of the problem.…”
“…Then G is isomorphic to a finite path, a simple cycle, a prism graph, a Möbius ladder, a particular graph as shown in Figure 1, or a quasi-ladder graph as shown in Figure 2. Bai, Lu and Yau [BLY21] classified all Ricci-flat graphs with maximum degree at most 4. Compared with our results, they require a more stringent curvature condition but relax the maximum degree condition from 3 to 4, which causes a huge increase in the complexity of the problem.…”
Graph Laplacians as well as related spectral inequalities and (co-)homology provide a foray into discrete analogues of Riemannian manifolds, providing a rich interplay between combinatorics, geometry and theoretical physics. We apply some of the latest techniques in data science such as supervised and unsupervised machine-learning and topological data analysis to the Wolfram database of some 8000 finite graphs in light of studying these correspondences. Encouragingly, we find that neural classifiers, regressors and networks can perform, with high efficiently and accuracy, a multitude of tasks ranging from recognizing graph Ricci-flatness, to predicting the spectral gap, to detecting the presence of Hamiltonian cycles, etc.
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