  Network data has become widespread, larger, and more complex over the years.
Traditional network data is dyadic, capturing the relations among pairs of
entities. With the need to model interactions among more than two entities,
significant research has focused on higher-order networks and ways to
represent, analyze, and learn from them. There are two main directions to
studying higher-order networks. One direction has focused on capturing
higher-order patterns in traditional (dyadic) graphs by changing the basic unit
of study from nodes to small frequently observed subgraphs, called motifs. As
most existing network data comes in the form of pairwise dyadic relationships,
studying higher-order structures within such graphs may uncover new insights.
The second direction aims to directly model higher-order interactions using new
and more complex representations such as simplicial complexes or hypergraphs.
Some of these models have long been proposed, but improvements in computational
power and the advent of new computational techniques have increased their
popularity. Our goal in this paper is to provide a succinct yet comprehensive
summary of the advanced higher-order network analysis techniques. We provide a
systematic review of its foundations and algorithms, along with use cases and
applications of higher-order networks in various scientific domains.
