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Modeling homophily and stochastic equivalence in symmetric relational data
TLDR
A latent variable model for inference and prediction of symmetric relational data, based on the idea of the eigenvalue decomposition, that generalizes other popular latent variable models.Abstract:
This article discusses a latent variable model for inference and prediction of symmetric relational data.
The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-specific vectors of latent characteristics. This ``eigenmodel'' generalizes other popular latent variable models, such as latent class and distance models: It is shown mathematically that any latent class or distance model has a representation as an eigenmodel, but not vice-versa. The practical implications of this are examined in the context of three real datasets, for which the eigenmodel has as good or better out-of-sample predictive performance than the other two models.read more
Citations
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Journal ArticleDOI
A Review of Relational Machine Learning for Knowledge Graphs
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A Survey of Statistical Network Models
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A nonparametric view of network models and Newman–Girvan and other modularities
Peter J. Bickel,Aiyou Chen +1 more
TL;DR: An attempt at unifying points of view and analyses of these objects coming from the social sciences, statistics, probability and physics communities are presented and the approach to the Newman–Girvan modularity, widely used for “community” detection, is applied.
Book ChapterDOI
Link prediction via matrix factorization
TL;DR: The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores, and may be combined with optional explicit features for nodes or edges, which yields better performance.
Journal ArticleDOI
Consistency of community detection in networks under degree-corrected stochastic block models
TL;DR: It is found that methods based on the degree-corrected stochastic block model are consistent under a wider class of models and that modularity-type methods require parameter constraints for consistency, whereas likelihood-based methods do not.
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Interaction network containing conserved and essential protein complexes in Escherichia coli
Gareth Butland,José M. Peregrín-Alvarez,Joyce Li,Wehong Yang,Xiaochun Yang,Veronica Canadien,Andrei Starostine,Dawn Richards,Bryan Beattie,Nevan J. Krogan,Michael Davey,John Parkinson,John Parkinson,Jack Greenblatt,Andrew Emili +14 more
TL;DR: Insight is provided into the function of previously uncharacterized bacterial proteins and the overall topology of a microbial interaction network, the core components of which are broadly conserved across Prokaryota.