  Our world produces massive data every day; they exist in diverse forms, from
pairwise data and matrix to time series and trajectories. Meanwhile, we have
access to the versatile toolkit of network analysis. Networks also have
different forms; from simple networks to higher-order network, each
representation has different capabilities in carrying information. For
researchers who want to leverage the power of the network toolkit, and apply it
beyond networks data to sequential data, diffusion data, and many more, the
question is: how to represent big data and networks? This dissertation makes a
first step to answering the question. It proposes the higher-order network,
which is a critical piece for representing higher-order interaction data; it
introduces a scalable algorithm for building the network, and visualization
tools for interactive exploration. Finally, it presents broad applications of
the higher-order network in the real-world.
