20.500.12556/DKUM-99334 A distribution-based framework for network similarity assessment Assessing structural similarity between complex systems represented as networks is a fundamental challenge across many disciplines. Existing methods range from exact graph matching to inexact approaches such as graph edit distance, graph kernels, and topological index-based comparisons. In this work, we introduce a new distribution-based framework for network comparison. Each net work is represented by its degree and distance distributions, which capture key structural features in probabilistic form. These distributions are compared using the Jensen-Shannon and Hellinger distance metrics. We further combine the degree- and distance-based dissimilarity measures into a unified similarity measure that captures complementary aspects of network structures. More over, we analyze its behavior on structured network families and demonstrate its applicability to both random and real-world networks, including molecular similarity assessment. network similarity measure degree distribution distance distribution Jensen-Shannon divergence random network models molecular structural similarity analiza omrežij primerjava omrežij strukturna podobnost porazdelitev stopenj Jensen-Shannonova razdalja molekularna strukturna podobnost true false true Elsevier Angleški jezik Slovenski jezik Znanstveno delo 2026-08-13 03:09:00 2026-09-02 12:43:02 2026-09-03 03:06:10 0000-00-00 00:00:00 2026 0 New York 0 11 str. št. članka 130179 Letn. 531 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 2026-05-22 2026-06-02 519.17 287591939 10.1016/j.amc.2026.130179 0096-3003 RAZ_Dehmer_Matthias_2026.pdf RAZ_Dehmer_Matthias_2026.pdf 1 4F87C02A01365EB03ACE62121FF9038B 281a1a8700fbd233863f4734579eb05e3fbce95727eb5d0b012c42a7112cfd3b fa233beb-a6ba-11f1-aef5-00155d0001be https://dk.um.si/Dokument.php?lang=slv&id=211286 https://doi.org/10.1016/j.amc.2026.130179 0 0aec39ec-a6bb-11f1-aef5-00155d0001be https://dk.um.si/Dokument.php?lang=slv&id=211287 Fakulteta za naravoslovje in matematiko Fakulteta za kemijo in kemijsko tehnologijo 0 0 0