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- W2783172499 abstract "Motif mining could recognize mecroscopic patterns of simple networks, especially with binary edges. Nevertheless, how to screen weighted motifs out of weighted networks is still a challenge. The difficulty mainly lies in two aspects, i.e. weight abstraction and null model enumeration. In this paper, we firstly model the heterogeneous weights as a fuzzy number problem, and normalize them into binary packages; then we neglect the time-consuming Markov process and discriminate weighted subgraphs by Bayes' equation. The simulation shows that our method is feasible and applicable." @default.
- W2783172499 created "2018-01-26" @default.
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- W2783172499 date "2017-11-01" @default.
- W2783172499 modified "2023-09-24" @default.
- W2783172499 title "Mine weighted network motifs via Bayes' theorem" @default.
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- W2783172499 doi "https://doi.org/10.1109/icsai.2017.8248334" @default.
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