Michelle Girvan

Statistical and Nonlinear Physics · 2001–2019

A researcher in the Science corpus: 9 papers; trusts 28 researchers and is trusted by 574 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 55 citations into it; #6 of 10,127 by trust here, #36 by citations
• Artificial Intelligence — 54 citations into it; #30,178 of 35,345 by trust here, #13,760 by citations
• Model Architectures — 24 citations into it
• Electrical and Electronic Engineering — 21 citations into it
• Molecular Biology — 16 citations into it; #48 of 17,773 by trust here, #1,296 by citations
• Computer Networks and Communications — 13 citations into it; #38 of 9,886 by trust here, #142 by citations
• Optimization & Training — 11 citations into it
• Experimental and Cognitive Psychology — 10 citations into it

Whom Michelle Girvan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Mark Newman
2. Steven H. Strogatz
3. David Lusseau
4. Duncan J. Watts
5. Ravindra K. Ahuja
6. U. Brandes
7. Réka Albert
8. Albert-László Barabási
9. Sepp Hochreiter
10. Paul J. Werbos

Who trusts Michelle Girvan overall

1. Mark Newman
2. Santo Fortunato
3. Clara Pizzuti
4. Jure Leskovec
5. Steve Gregory
6. Alex Arenas
7. Chaomei Chen
8. Roger Guimerà
9. Nees Jan van Eck
10. Olaf Sporns

Papers from Michelle Girvan's perspective

Ordered by how strongly the researchers Michelle Girvan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
8. Human-level control through deep reinforcement learning
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Community structure in social and biological networks (2001) — 15,867 citations
• Finding and evaluating community structure in networks. (2003) — 14,008 citations
• Policing stabilizes construction of social niches in primates (2006) — 642 citations
• Structure of growing social networks. (2001) — 557 citations
• Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics (2019) — 503 citations
• Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach. (2018) — 1,176 citations
• Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model (2018) — 350 citations
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. (2017) — 674 citations
• The myopia of crowds: Cognitive load and collective evaluation of answers on Stack Exchange (2016) — 33 citations