To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities.
This paper proposes a novel model for predicting subgraphs in dynamic graphs, an extension of traditional link prediction.
We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs.
In this paper, we propose a co-analysis framework based on biclusters, which are two subsets of variables and voxels with close scalar-value relationships, to guide the process of visually exploring multivariate data.
Network embeddings learn to represent nodes as low-dimensional vectors to preserve the proximity between nodes and communities of the network for network analysis.