Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

We review some of the literature on semi-supervised learning in this paper. Traditional
classifiers need labeled data (feature/label pairs) to train. Labeled instances however are …

We introduce a self-supervised approach for learning node and graph level representations
by contrasting structural views of graphs. We show that unlike visual representation learning …

We investigate the representation power of graph neural networks in the semi-supervised
node classification task under heterophily or low homophily, ie, in networks where …

Various graph contrastive learning models have been proposed to improve the performance
of tasks on graph datasets in recent years. While effective and prevalent, these models are …

In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

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 …

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 …

Abstract Graph Neural Networks (GNNs) have proven to be useful for many different
practical applications. However, many existing GNN models have implicitly assumed …