about
Efficient discovery of overlapping communities in massive networksLongitudinal Mixed Membership Trajectory Models for Disability Survey DataEstimating Identification Disclosure Risk Using Mixed Membership ModelsDetecting overlapping protein complexes based on a generative model with functional and topological propertiesRANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKSCluster and propensity based approximation of a networkResolving the structure of interactomes with hierarchical agglomerative clusteringA Poisson model for random multigraphs.The analysis of social network data: an exciting frontier for statisticians.Testing for nodal dependence in relational data matricesHow Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience.Dynamic networks from hierarchical bayesian graph clustering.A digital network approach to infer sex behavior in emerging HIV epidemicsSmall sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoningBootstrapping on undirected binary networks via statistical mechanics.The interplay between microscopic and mesoscopic structures in complex networks.Likelihoods for fixed rank nomination networks.Efficiently inferring community structure in bipartite networks.Network histograms and universality of blockmodel approximation.Reconceptualizing the classification of PNAS articles.BiomeNet: a Bayesian model for inference of metabolic divergence among microbial communitiesRevealing the hidden relationship by sparse modules in complex networks with a large-scale analysis.Community extraction for social networks.Compressive Network Analysis.A latent parameter node-centric model for spatial networks.Network Sampling and Classification:An Investigation of Network Model Representations.Combined node and link partitions method for finding overlapping communities in complex networks.Identification of hybrid node and link communities in complex networksA heuristic approach to determine an appropriate number of topics in topic modelingHow networks change with time.Local dependence in random graph models: characterization, properties and statistical inference.MODEL-BASED CLUSTERING OF LARGE NETWORKS.Selecting cases for whom additional tests can improve prognostication.Finding Communities by Their Centers.Stochastic blockmodels with a growing number of classesComputational Statistical Methods for Social Network Models.Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions.A multi-similarity spectral clustering method for community detection in dynamic networks.A nonparametric view of network models and Newman-Girvan and other modularities.A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures.
P2860
Q24626714-44F9E9A5-3A81-43E9-B881-3CCAA94CD37CQ27347304-F8F014F4-1DC3-4361-A32F-DC2CDDB37EF1Q28655921-1DAEF60A-BFEE-42F0-9166-062B78FB2834Q28657363-BF526D59-5757-4563-B2F1-1D443BA1820CQ28658592-55D4B1FC-BC47-4C02-BD38-08EF9FA9DCF7Q28686900-C88F1655-8DE0-4BFC-91D3-597EB8AC91E8Q28742433-58247985-254B-4A28-84F6-FBD276AFF7F4Q28743326-13D97787-6F21-4727-A8CF-83A3BD35E333Q30571341-CF027C53-46DC-4978-BDDC-21D9F2A641C8Q31079421-019441BE-FD69-4083-A246-17B1A70F4BB3Q31119341-B1B5C45C-279D-4A00-AAD5-2F7475C573C4Q33525021-EAC60118-A757-4659-84B1-874EE272B3BFQ33844959-1B83839B-35EF-4C11-A7C7-B56F4ED71C85Q33936105-277A987D-B5FE-4FEB-962A-1A99920A5CC0Q33951483-B8497A04-2FB5-4767-9FD3-9BDE16EE8FF0Q33988292-8B91BC89-D17F-4B79-B0DB-33014ADDA7E3Q34004161-56B1F53F-F8F0-4842-9CC6-0F263BB4D2BDQ34063507-06E78A36-33CB-4CA4-B3FB-F514A774A8B2Q34384167-11B0EA3C-0AC2-4454-A8DC-E0369B94716DQ34397325-AE3D2301-8FBF-4919-B5A2-5315D6E8D35DQ34549449-E8B3082D-35A8-4361-8B04-AE662E6369F6Q34770400-D3089952-4D6B-469A-AA77-41D1A4D92B54Q34937535-6A571C0E-3B9D-445E-BF72-F0CF4DB0B644Q34994201-14FC1E60-0659-43FF-8922-B8BC3B3D00D2Q35004611-0CAFEB37-9DA5-4C35-AC8E-DCAAC00B3C50Q35033203-C85A9FF6-7A62-43C5-87FF-89B580BDFA8DQ35123595-D4A6494F-9844-48DE-A6A3-03889697E7FFQ35134004-BEBB41F4-F290-450A-86BD-C064F1DE9282Q35794507-65040BA5-AB07-415A-AF41-DFD57FF13746Q36022118-0DFD39D6-F591-493E-9E15-463D65511F98Q36262815-3B513133-DD80-4092-B9B5-94EB9402D372Q36305056-C84B1632-6085-4984-A5B0-19A0DD3CD907Q36519219-A8D42ED9-8790-4CD5-BC4C-DA1377F5BE83Q36774364-F4898E20-2266-493A-B431-32218E69094BQ36793375-4FBA2E25-DCB2-4074-86BE-FFC921BC8A17Q36969246-0754BAB5-B983-44C6-AEDD-7C51823F42D4Q37026465-04BBB1FF-C720-43C3-AA03-C4CEE01EC881Q37177326-07096554-0CE7-430F-B663-827E2CF0648FQ37482140-B8EEB5E7-DC27-40D5-854E-C389D863D928Q37501383-756C2632-EEDE-4F78-8AA7-D853CE521EAC
P2860
description
2008 nî lūn-bûn
@nan
2008 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Mixed Membership Stochastic Blockmodels
@ast
Mixed Membership Stochastic Blockmodels
@en
type
label
Mixed Membership Stochastic Blockmodels
@ast
Mixed Membership Stochastic Blockmodels
@en
prefLabel
Mixed Membership Stochastic Blockmodels
@ast
Mixed Membership Stochastic Blockmodels
@en
P2860
P1476
Mixed Membership Stochastic Blockmodels.
@en
P2093
Edoardo M Airoldi
Eric P Xing
P2860
P304
P407
P577
2008-09-01T00:00:00Z