We design algorithms for fitting a high-dimensional statistical model to a large, sparse network without revealing sensitive information of individual members. Given a sparse input graph G, our ...
We study graphons as a non-parametric generalization of stochastic block models, and show how to obtain compactly represented estimators for sparse networks in this framework. Our algorithms and ...
Abstract: Compared with Static Knowledge Graphs, Temporal Knowledge Graphs need to pay more attention to the time when facts occur and these facts will change over time. However, existing models lack ...
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