Real-world networks often consist of millions of heterogenous elements that interact at
multiple timescales and length scales. The fields of statistical physics and control theory both …

The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …

This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …

Graph neural networks (GNNs) rely on graph convolutions to extract local features from
network data. These graph convolutions combine information from adjacent nodes using …

Numerous recent works have analyzed the expressive power of message-passing graph
neural networks (MPNNs), primarily utilizing combinatorial techniques such as the $1 …

Consider the problem of estimating the entries of a large matrix, when the observed entries
are noisy versions of a small random fraction of the original entries. This problem has …

Recently, it became apparent that a large number of the most interesting structures and
phenomena of the world can be described by networks. To develop a mathematical theory of …

This paper focuses on spectral graph convolutional neural networks (ConvNets), where
filters are defined as elementwise multiplication in the frequency domain of a graph. In …

We introduce a method for the theoretical analysis of exponential random graph models.
The method is based on a large-deviations approximation to the normalizing constant …

This paper proposes an empirical model of network formation, combining strategic and
random networks features. Payoffs depend on direct links, but also link externalities. Players …