Core-periphery Models for Hypergraphs

1 code implementation • 1 Jun 2022 • Marios Papachristou, Jon Kleinberg

Our inference algorithm is capable of learning embeddings that correspond to the reputation (rank) of a node within the hypergraph.

Sublinear Domination and Core-Periphery Networks

1 code implementation • 4 Mar 2021 • Marios Papachristou

In this paper we devise a generative random network model with core-periphery properties whose core nodes act as sublinear dominators, that is, if the network has $n$ nodes, the core has size $o(n)$ and dominates the entire network.

Social and Information Networks

Truncated Log-concave Sampling with Reflective Hamiltonian Monte Carlo

1 code implementation • 25 Feb 2021 • Apostolos Chalkis, Vissarion Fisikopoulos, Marios Papachristou, Elias Tsigaridas

We introduce Reflective Hamiltonian Monte Carlo (ReHMC), an HMC-based algorithm, to sample from a log-concave distribution restricted to a convex body.