Generating Simple Directed Social Network Graphs for Information Spreading
Christoph Schweimer, Christine Gfrerer, Florian Lugstein and, David Pape, Jan A. Velimsky, Robert Els\"asser, Bernhard C. Geiger

TL;DR
This paper introduces a scalable method to generate directed social network graphs with reciprocal edges and realistic properties, useful for analyzing information spread and epidemic models.
Contribution
The authors propose a novel graph generation approach that models directed and reciprocal edges, capturing real-world social network properties more accurately than existing models.
Findings
Generated graphs match real Twitter data in clustering and distance metrics.
The degree sequences follow a chi-squared distribution, not a power law.
Simulations on generated graphs replicate real-world information dissemination behaviors.
Abstract
Online social networks are a dominant medium in everyday life to stay in contact with friends and to share information. In Twitter, users can connect with other users by following them, who in turn can follow back. In recent years, researchers studied several properties of social networks and designed random graph models to describe them. Many of these approaches either focus on the generation of undirected graphs or on the creation of directed graphs without modeling the dependencies between reciprocal (i.e., two directed edges of opposite direction between two nodes) and directed edges. We propose an approach to generate directed social network graphs that creates reciprocal and directed edges and considers the correlation between the respective degree sequences. Our model relies on crawled directed graphs in Twitter, on which information w.r.t. a topic is exchanged or disseminated.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
