The use of online social networks has made significant progress in recent years as the use
of the Internet has become widespread worldwide as the technological infrastructure and the …

In many domains where data are represented as graphs, learning a similarity metric among
graphs is considered a key problem, which can further facilitate various learning tasks, such …

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 …

Graph-structured data arise naturally in many different application domains. By representing
data as graphs, we can capture entities (ie, nodes) as well as their relationships (ie, edges) …

Problems involving multiple networks are prevalent in many scientific and other domains. In
particular, network alignment, or the task of identifying corresponding nodes in different …

Network embedding aims to preserve vertex similarity in an embedding space. Existing
approaches usually define the similarity by direct links or common neighborhoods between …

In this work, we present a method for node embedding in temporal graphs. We propose an
algorithm that learns the evolution of a temporal graph's nodes and edges over time and …

From social science to biology, numerous applications often rely on graphlets for intuitive
and meaningful characterization of networks at both the global macro-level as well as the …

Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Such temporal networks (or edge streams) consist of a sequence of …

Structural roles define sets of structurally similar nodes that are more similar to nodes inside
the set than outside, whereas communities define sets of nodes with more connections …