Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

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 …

This book is a foundational guide to graph representation learning, including state-of-the art
advances, and introduces the highly successful graph neural network (GNN) formalism …

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

Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

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 …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …