DAGSHOP — A Workshop on directed acyclic graphs

Time: June 2nd, 8:30-12:30, Location: Faculty of Arts and Social Sciences, Grote Gracht 76, hall 0.07

SCHEDULE

Session 1: Chair Mikko Kivelä

8:30 — 9:10 Tim Evans: DAGology
9:10 — 9:50 Marian Boguña: Causal sets and the fabric of spacetime. A minimalistic approach to quantum gravity
9:50 — 10:10 Arash Badie-Modiri: Event graph representation of temporal hypergraphs
10:10 — 10:30 Niels van Santen: DAGs are network models of mechanisms, what can we learn by looking at the resulting behaviour?

10:30 — 11:00 Coffee break

Session 2: Chair Márton Karsai

11:00 — 11:40 Ingo Scholtes: Directed Acyclic Graphs, Time and Causality in Temporal Networks
11:40 — 12:00 Omar Henderson: The Temporal Event Graph as Tool for Modelling Spreading Processes
12:00 — 12:20 To be announced

CONTENT

Directed Acyclic Graphs (DAGs) appear in many contexts in network science, from citation networks and pedigrees to temporal-network event graphs and representations of discretized spacetime in quantum gravity. They are also widely used in machine learning, e.g., in Bayesian networks. The particular strength of DAGs comes from their capability to represent temporal and causal structures: e.g., the time-respecting paths of temporal networks and the causal structure of spacetime with future and past lightcones can be readily mapped onto DAGs. However, at the time being, research on DAGs is scattered across different subfields of network science, mathematics, and computer science. The purpose of DAGshop is to unite these various lines of research and gather network scientists interested in learning about the properties and use of directed acyclic graphs.

The scope of the satellite symposium is to explore and showcase the potential of DAG-based approaches in network science. In this spirit, the satellite aims to create the opportunity for cross-pollination between different disciplines and to build an interdisciplinary community around this topic.

Topics covered by DAGShop include:

• Causality in networks
• Temporal event graphs
• Temporal-network embeddings
• Directed acyclic graphs in machine learning
• Citation networks
• Pedigrees and family trees
• DAGs in spacetime physics and quantum gravity