Matches in SemOpenAlex for { <https://semopenalex.org/work/W2749134888> ?p ?o ?g. }
- W2749134888 abstract "Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing likelihood for a special class of inner-product models while working simultaneously for a wide range of different latent space models, such as distance models, which allow latent vectors to affect edge formation in flexible ways. These fitting methods, especially the one based on projected gradient descent, are fast and scalable to large networks. We obtain their rates of convergence for both inner-product models and beyond. The effectiveness of the modeling approach and fitting algorithms is demonstrated on five real world network datasets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning." @default.
- W2749134888 created "2017-08-31" @default.
- W2749134888 creator A5077607729 @default.
- W2749134888 creator A5083090411 @default.
- W2749134888 date "2017-05-05" @default.
- W2749134888 modified "2023-09-25" @default.
- W2749134888 title "Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting" @default.
- W2749134888 cites W1469808163 @default.
- W2749134888 cites W1475335089 @default.
- W2749134888 cites W1788531841 @default.
- W2749134888 cites W1827214880 @default.
- W2749134888 cites W1848620603 @default.
- W2749134888 cites W1884015253 @default.
- W2749134888 cites W1971215074 @default.
- W2749134888 cites W2019144999 @default.
- W2749134888 cites W2032005951 @default.
- W2749134888 cites W2050958540 @default.
- W2749134888 cites W2054323527 @default.
- W2749134888 cites W2054553473 @default.
- W2749134888 cites W2064274783 @default.
- W2749134888 cites W2066459332 @default.
- W2749134888 cites W2096091969 @default.
- W2749134888 cites W2102019642 @default.
- W2749134888 cites W2112976607 @default.
- W2749134888 cites W2116680824 @default.
- W2749134888 cites W2123400271 @default.
- W2749134888 cites W2124608575 @default.
- W2749134888 cites W2129923964 @default.
- W2749134888 cites W2134265283 @default.
- W2749134888 cites W2134332047 @default.
- W2749134888 cites W2135825502 @default.
- W2749134888 cites W2139327160 @default.
- W2749134888 cites W2150256170 @default.
- W2749134888 cites W2152284345 @default.
- W2749134888 cites W2159203990 @default.
- W2749134888 cites W2162630660 @default.
- W2749134888 cites W2192240806 @default.
- W2749134888 cites W2320533836 @default.
- W2749134888 cites W2396019715 @default.
- W2749134888 cites W2399402693 @default.
- W2749134888 cites W2616032753 @default.
- W2749134888 cites W2618599435 @default.
- W2749134888 cites W2913243980 @default.
- W2749134888 cites W2962769133 @default.
- W2749134888 cites W2963246299 @default.
- W2749134888 cites W2963264888 @default.
- W2749134888 cites W2963974511 @default.
- W2749134888 cites W2964200481 @default.
- W2749134888 cites W3100144903 @default.
- W2749134888 cites W3106324661 @default.
- W2749134888 cites W1894984256 @default.
- W2749134888 cites W2106088750 @default.
- W2749134888 cites W2144730813 @default.
- W2749134888 doi "https://doi.org/10.48550/arxiv.1705.02372" @default.
- W2749134888 hasPublicationYear "2017" @default.
- W2749134888 type Work @default.
- W2749134888 sameAs 2749134888 @default.
- W2749134888 citedByCount "11" @default.
- W2749134888 countsByYear W27491348882018 @default.
- W2749134888 countsByYear W27491348882019 @default.
- W2749134888 countsByYear W27491348882020 @default.
- W2749134888 countsByYear W27491348882021 @default.
- W2749134888 crossrefType "posted-content" @default.
- W2749134888 hasAuthorship W2749134888A5077607729 @default.
- W2749134888 hasAuthorship W2749134888A5083090411 @default.
- W2749134888 hasBestOaLocation W27491348881 @default.
- W2749134888 hasConcept C104122410 @default.
- W2749134888 hasConcept C11413529 @default.
- W2749134888 hasConcept C114614502 @default.
- W2749134888 hasConcept C119043178 @default.
- W2749134888 hasConcept C119857082 @default.
- W2749134888 hasConcept C153258448 @default.
- W2749134888 hasConcept C154945302 @default.
- W2749134888 hasConcept C162307627 @default.
- W2749134888 hasConcept C162324750 @default.
- W2749134888 hasConcept C191399111 @default.
- W2749134888 hasConcept C194648359 @default.
- W2749134888 hasConcept C2777303404 @default.
- W2749134888 hasConcept C2779812341 @default.
- W2749134888 hasConcept C33923547 @default.
- W2749134888 hasConcept C41008148 @default.
- W2749134888 hasConcept C50522688 @default.
- W2749134888 hasConcept C50644808 @default.
- W2749134888 hasConcept C51167844 @default.
- W2749134888 hasConcept C93959086 @default.
- W2749134888 hasConceptScore W2749134888C104122410 @default.
- W2749134888 hasConceptScore W2749134888C11413529 @default.
- W2749134888 hasConceptScore W2749134888C114614502 @default.
- W2749134888 hasConceptScore W2749134888C119043178 @default.
- W2749134888 hasConceptScore W2749134888C119857082 @default.
- W2749134888 hasConceptScore W2749134888C153258448 @default.
- W2749134888 hasConceptScore W2749134888C154945302 @default.
- W2749134888 hasConceptScore W2749134888C162307627 @default.
- W2749134888 hasConceptScore W2749134888C162324750 @default.
- W2749134888 hasConceptScore W2749134888C191399111 @default.
- W2749134888 hasConceptScore W2749134888C194648359 @default.
- W2749134888 hasConceptScore W2749134888C2777303404 @default.
- W2749134888 hasConceptScore W2749134888C2779812341 @default.
- W2749134888 hasConceptScore W2749134888C33923547 @default.
- W2749134888 hasConceptScore W2749134888C41008148 @default.