The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …

The diversity of nodes in a complex network causes each node to have varying significance,
and the important nodes often have a significant impact on the structure and function of the …

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 …

Using graphs to model pairwise relationships between entities is a ubiquitous framework for
studying complex systems and data. Simplicial complexes extend this dyadic model of …

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

Graph neural networks (GNNs) are information processing architectures for signals
supported on graphs. They are presented here as generalizations of convolutional neural …

In this paper, we analyze linear-quadratic stochastic differential games with a continuum of
players interacting through graphon aggregates, each state being subject to idiosyncratic …

Graphons are infinite-dimensional objects that represent the limit of convergent sequences
of graphs as their number of nodes goes to infinity. This paper derives a theory of graphon …

Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and
pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably …

Recently, the paradigm of pre-training and fine-tuning graph neural networks has been
intensively studied and applied in a wide range of graph mining tasks. Its success is …