We present a novel technique for cardinality-constrained index-tracking, a common task in the financial industry. Our approach is based on market graph models. We model our reference indices as market graphs and express the index-tracking problem as a quadratic K-medoids clustering problem. We...

We characterize the dynamics of neighborhood racial composition by using the k-medians machine learning technique to group neighborhoods into five different patterns according to the evolution of the Black population share of census tracts from 1950 through 1990. The procedure classifies tracts...

It has often been observed that there is substantial spatial variation in criminality, i.e. criminality clusters in neighborhoods. Differences in neighborhood characteristics are one possible reason, social interactions another. In this paper we use detailed data on the residential location of...

We propose a random network model incorporating heterogeneity of agents and a continuous notion of homophily. Unlike the vast majority of the corresponding economic literature, we capture homophily in terms of similarity rather than equality by assuming that the probability of linkage between...

The communities in a network have distinct characteristics and interrelationships. Community discovery methods based on node embedding and deep learning have surpassed spectral clustering and statistical inference as the preferred methods for handling high-dimensional network data. Community...