  Graphs are widely used as a popular representation of the network structure
of connected data. Graph data can be found in a broad spectrum of application
domains such as social systems, ecosystems, biological networks, knowledge
graphs, and information systems. With the continuous penetration of artificial
intelligence technologies, graph learning (i.e., machine learning on graphs) is
gaining attention from both researchers and practitioners. Graph learning
proves effective for many tasks, such as classification, link prediction, and
matching. Generally, graph learning methods extract relevant features of graphs
by taking advantage of machine learning algorithms. In this survey, we present
a comprehensive overview on the state-of-the-art of graph learning. Special
attention is paid to four categories of existing graph learning methods,
including graph signal processing, matrix factorization, random walk, and deep
learning. Major models and algorithms under these categories are reviewed
respectively. We examine graph learning applications in areas such as text,
images, science, knowledge graphs, and combinatorial optimization. In addition,
we discuss several promising research directions in this field.
