Random walks are ubiquitous in the sciences, and they are interesting from both theoretical
and practical perspectives. They are one of the most fundamental types of stochastic …

While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …

Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
However, there is relatively little understanding of why GNNs are successful in practice and …

Learning representations of sets of nodes in a graph is crucial for applications ranging from
node-role discovery to link prediction and molecule classification. Graph Neural Networks …

We present network embedding algorithms that capture information about a node from the
local distribution over node attributes around it, as observed over random walks following an …

Graph Neural Networks (GNNs) are an effective framework for representation learning of
graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector …

In this paper, we propose a flexible notion of characteristic functions defined on graph
vertices to describe the distribution of vertex features at multiple scales. We introduce …

Machine learning on graphs is an important and ubiquitous task with applications ranging
from drug design to friendship recommendation in social networks. The primary challenge in …