Topology reveals universal features for network comparison
May 16, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Pierre-AndrΓ© G. Maugis, Sofia C. Olhede, Patrick J. Wolfe
arXiv ID
1705.05677
Category
stat.ME
Cross-listed
cs.SI,
math.CO,
math.ST
Citations
11
Venue
arXiv.org
Last Checked
1 month ago
Abstract
The topology of any complex system is key to understanding its structure and function. Fundamentally, algebraic topology guarantees that any system represented by a network can be understood through its closed paths. The length of each path provides a notion of scale, which is vitally important in characterizing dominant modes of system behavior. Here, by combining topology with scale, we prove the existence of universal features which reveal the dominant scales of any network. We use these features to compare several canonical network types in the context of a social media discussion which evolves through the sharing of rumors, leaks and other news. Our analysis enables for the first time a universal understanding of the balance between loops and tree-like structure across network scales, and an assessment of how this balance interacts with the spreading of information online. Crucially, our results allow networks to be quantified and compared in a purely model-free way that is theoretically sound, fully automated, and inherently scalable.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β stat.ME
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology
R.I.P.
π»
Ghosted
External Validity: From Do-Calculus to Transportability Across Populations
R.I.P.
π»
Ghosted
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
R.I.P.
π»
Ghosted
Doubly Robust Policy Evaluation and Optimization
R.I.P.
π»
Ghosted
Comparison of Bayesian predictive methods for model selection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted