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• W3200327369 abstract "This paper considers the problem of modeling and estimating community memberships of nodes in a directed network where every row (column) node is associated with a vector determining its membership in each row (column) community. To model such directed network, we propose directed degree corrected mixed membership (DiDCMM) model by considering degree heterogeneity. DiDCMM is identifiable under popular conditions for mixed membership network when considering degree heterogeneity. Based on the cone structure inherent in the normalized version of the left singular vectors and the simplex structure inherent in the right singular vectors of the population adjacency matrix, we build an efficient algorithm called DiMSC to infer the community membership vectors for both row nodes and column nodes. By taking the advantage of DiMSC's equivalence algorithm which returns same estimations as DiMSC and the recent development on row-wise singular vector deviation, we show that the proposed algorithm is asymptotically consistent under mild conditions by providing error bounds for the inferred membership vectors of each row node and each column node under DiDCMM. The theory is supplemented by a simulation study." @default.
• W3200327369 created "2021-09-27" @default.
• W3200327369 creator A5039186424 @default.
• W3200327369 date "2021-09-16" @default.
• W3200327369 modified "2023-09-24" @default.
• W3200327369 title "Directed degree corrected mixed membership model and estimating community memberships in directed networks" @default.
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• W3200327369 doi "https://doi.org/10.48550/arxiv.2109.07826" @default.
• W3200327369 hasPublicationYear "2021" @default.
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