• Publications
  • Influence
Finding and evaluating community structure in networks.
  • M. Newman, M. Girvan
  • Computer Science
    Physical review. E, Statistical, nonlinear, and…
  • 11 August 2003
TLDR
It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Community structure in social and biological networks
  • M. Girvan, M. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences…
  • 7 December 2001
TLDR
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Policing stabilizes construction of social niches in primates
TLDR
Policing not only controls conflict, it significantly influences the structure of networks that constitute essential social resources in gregarious primate societies, and plays a critical role in infant survivorship, emergence and spread of cooperative behaviour, social learning and cultural traditions.
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach.
We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from
Structure of growing social networks.
TLDR
Using computer simulations, it is found that models that incorporate all of these features reproduce many of the features of real social networks, including high levels of clustering or network transitivity and strong community structure in which individuals have more links to others within their community than to individuals from other communities.
Reservoir observers: Model-free inference of unmeasured variables in chaotic systems.
TLDR
It is shown that the reservoir observer can be a very effective and versatile tool for robustly reconstructing unmeasured dynamical system variables.
Mixing Patterns and Community Structure in Networks
TLDR
A new computer algorithm is described that detects structure of this kind in real-world networks and shows that they do indeed possess non-trivial community structure, and a possible explanation for this structure in the mechanism of assortative mixing.
The size of the sync basin.
TLDR
Results on the sync basin for a ring of n >> 1 identical phase oscillators with sinusoidal coupling are reported, revealing that their basin sizes obey a tantalizingly simple statistical law: the probability that the final state has q twists follows a Gaussian distribution with respect to q.
Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data.
TLDR
This work uses recent advances in the machine learning area known as "reservoir computing" to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process to form a modified autonomous reservoir.
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model
TLDR
A general method is proposed that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme, and is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.
...
...