Several research studies have shown that complex networks modeling real-world
phenomena are characterized by striking properties:(i) they are organized according to …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Human social interactions in local settings can be experimentally detected by recording the
physical proximity and orientation of people. Such interactions, approximating face-to-face …

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 …

Computing a (short) path between two vertices is one of the most fundamental primitives in
graph algorithmics. In recent years, the study of paths in temporal graphs, that is, graphs …

We investigate consensus dynamics on temporal hypergraphs that encode network systems
with time-dependent, multiway interactions. We compare these consensus processes with …

The influence maximization problem has gained increasing attention in recent years.
Previous research focuses on the development of algorithms to analyze static social …

Parallel Aggregated Ordered Hypergraph (PAOH) is a novel technique to visualize dynamic
hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several …

Many dynamical systems can be successfully analyzed by representing them as networks.
Empirically measured networks and dynamic processes that take place in these situations …

Temporal graphs have time-stamped edges. Building on previous work, we study the
problem of finding a small vertex set (the separator) whose removal destroys all temporal …