| Title: | A distribution-based framework for network similarity assessment |
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| Authors: | ID Dehmer, Matthias (Author)
ID Redžepović, Izudin (Author)
ID Tratnik, Niko (Author)
ID Žigert Pleteršek, Petra (Author) |
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| Files: |  RAZ_Dehmer_Matthias_2026.pdf (3,24 MB)
MD5: 4F87C02A01365EB03ACE62121FF9038B
 https://doi.org/10.1016/j.amc.2026.130179
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FNM - Faculty of Natural Sciences and Mathematics
FKKT - Faculty of Chemistry and Chemical Engineering
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| Abstract: | 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. |
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| Keywords: | network similarity measure, degree distribution, distance distribution, Jensen-Shannon divergence, random network models, molecular structural similarity |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Article acceptance date: | 22.05.2026 |
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| Publication date: | 02.06.2026 |
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| Place of publishing: | New York |
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| Publisher: | Elsevier |
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| Year of publishing: | 2026 |
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| Number of pages: | 11 str. |
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| Numbering: | Letn. 531, št. članka 130179 |
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| PID: | 20.500.12556/DKUM-99334  |
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| UDC: | 519.17 |
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| ISSN on article: | 0096-3003 |
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| COBISS.SI-ID: | 287591939 |
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| DOI: | 10.1016/j.amc.2026.130179 |
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| Publication date in DKUM: | 02.09.2026 |
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| Views: | 248 |
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| Downloads: | 0 |
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| Metadata: |    |
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| Categories: | Misc.
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