A network is a collection of nodes and edges, where an edge connects two nodes. Many
social, natural and engineered systems can be represented as networks. Examples include …

I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few
methods and ideas, including several of personal interest, that I anticipate to be especially …

Networks are well-established representations of social systems, and temporal networks are
widely used to study their dynamics. However, going from temporal network data (ie a …

Characterizing the importances (ie, centralities) of nodes in social, biological, and
technological networks is a core topic in both network analysis and data science. We …

Temporal link prediction (TLP) aims to predict future links and is attracting increasing
attention. The diverse interaction patterns and nonlinear nature of temporal networks make it …

In many application settings involving networks, such as messages between users of an on-
line social network or transactions between traders in financial markets, the observed data …

Increasing amounts of data are available on temporal, or time-varying, networks. There have
been various representations of temporal network data each of which has different …

Abstract In classical Social Network Analysis (SNA), what counted as a “social tie” was fixed
by available data collection methods. The emergence of large-scale unobtrusive data …

Temporal network data are increasingly available in various domains, and often represent
highly complex systems with intricate structural and temporal evolutions. Due to the difficulty …

Finding dense subnetworks, with density based on edges or more complex structures, such
as subgraphs or k-cliques, is a fundamental algorithmic problem with many applications …