Directed degree corrected mixed membership model and estimating community memberships in directed networks
This work addresses community detection in directed networks, an incremental advancement for researchers in network analysis and machine learning.
The paper tackles the problem of modeling and estimating community memberships in directed networks by proposing the directed degree corrected mixed membership (DiDCMM) model, which accounts for degree heterogeneity and is identifiable under common conditions, and develops an efficient algorithm called DiMSC that provides asymptotically consistent error bounds for inferred membership vectors.
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.