Abstract: When distribution shifts occur between testing and training graph data, out-of-distribution (OOD) samples undermine the performance of graph neural networks (GNNs). To improve adaptive OOD ...
Abstract: As a promising strategy to achieve generalizable graph learning tasks, graph invariant learning emphasizes identifying invariant subgraphs for stable predictions on biased unknown ...
This is an instruction on using our softwares for computations related to graph invariants: [t,p]-spectrum and its induced indices, where the former was introduced by Ricky Chen while the latter was ...
This is a preview. Log in through your library . Abstract In the recent literature there are numerous publications concerned with a graph invariant named “Gutman index” (ZZ). In this paper, some ...
Fullerenes are hollow carbon molecules where each atom is connected to exactly three other atoms, arranged in pentagonal and hexagonal rings. Mathematically, they can be combinatorially modeled as ...
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