We present a causality-inspired adjusted plus-minus model for evaluating individual players from their performance on a team. We take an explicitly causal approach to this problem, defining the value of a player to be the expected change in the score …
In community detection, datasets often suffer a sampling bias for which nodes which would normally have a high affinity appear to have zero affinity. This happens for example when two affine users of a social network were not exposed to one another. …
We consider the problem of estimating social influence, the effect that a …
We study network properties of networks evolving in time based on optimal transport principles. These evolve from a structure covering uniformly a continuous space towards an optimal design in terms of optimal transport theory. At convergence, the …
We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goalis to estimate this global centrality measure having at disposal a limited amount of data. This is the case inmany real-world scenarios where data …
We consider the problem of Gaussian mixture clustering in the high-dimensional limit where the data consists of $m$ points in $n$ dimensions, $n,m\rightarrow\infty$ and $\alpha=m/n$ stays finite. Using exact but non-rigorous methods from statistical …
We propose a new look at the heat bath for two Brownian particles, in which the heat bath as a `system' is both perturbed and sensed by the Brownian particles. Non-local thermal fluctuation give rise to bath-mediated static forces between the …