A great variety of systems in nature, society and technology–from the web of sexual contacts
to the Internet, from the nervous system to power grids–can be modeled as graphs of …

The power of any kind of network approach lies in the ability to simplify a complex system so
that one can better understand its function as a whole. Sometimes it is beneficial, however …

Temporal networks serve as abstractions of many real-world dynamic systems. These
networks typically evolve according to certain laws, such as the law of triadic closure, which …

Network analysis is widely used in diverse fields and can be a powerful framework for
studying the structure of biological systems. Temporal dynamics are a key issue for many …

In many populations, the patterns of potentially infectious contacts are transients that can be
described as a network with dynamic links. The relative timescales of link and contagion …

This work copes with the problem of link prediction in large-scale two-mode social networks.
Two variations of the link prediction tasks are studied: predicting links in a bipartite graph …

Pattern counting in graphs is fundamental to several network sci-ence tasks, and there is an
abundance of scalable methods for estimating counts of small patterns, often called motifs …

Temporal hypergraphs provide a powerful paradigm for modeling time-dependent, higher-
order interactions in complex systems. Representation learning for hypergraphs is essential …

From sensor networks to transportation infrastructure to social networks, we are awash in
data. Many of these real-world networks tend to be large (“big data”) and dynamic, evolving …

Dynamic systems that consist of a set of interacting elements can be abstracted as temporal
networks. Recently, higher-order patterns that involve multiple interacting nodes have been …