Deepayan Chakrabarti

Statistical and Nonlinear Physics · 2003–2011

A researcher in the Science corpus: 8 papers; trusts 46 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 111 citations into it; #553 of 10,127 by trust here, #514 by citations
• Computer Networks and Communications — 40 citations into it; #5,090 of 9,886 by trust here, #5,518 by citations
• Artificial Intelligence — 34 citations into it; #8,636 of 35,345 by trust here, #10,942 by citations
• Information Systems — 25 citations into it
• Algorithms & Complexity — 19 citations into it
• Molecular Biology — 13 citations into it; #4,774 of 17,773 by trust here, #9,136 by citations
• Sociology and Political Science — 11 citations into it
• Computational Theory and Mathematics — 10 citations into it; #8,056 of 12,182 by trust here, #5,724 by citations

Whom Deepayan Chakrabarti trusts overall

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

1. Jon M. Kleinberg
2. Béla Bollobás
3. Jure Leskovec
4. Mark Newman
5. Albert-László Barabási
6. Romualdo Pastor-Satorras
7. Duncan J. Watts
8. Réka Albert
9. Christos Faloutsos
10. Alessandro Vespignani

Who trusts Deepayan Chakrabarti overall

1. Jure Leskovec
2. Charu C. Aggarwal
3. Christos Faloutsos
4. Saket Navlakha
5. Yuanyuan Tian
6. Manuel Gomez-Rodriguez
7. Hanghang Tong
8. Lei Tang
9. Leman Akoglu
10. Sungpack Hong

Papers from Deepayan Chakrabarti's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Collective dynamics of ‘small-world’ networks
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication (2005) — 400 citations
• Kronecker Graphs: An Approach to Modeling Networks (2008) — 1,118 citations
• Event Summarization Using Tweets (2011) — 362 citations
• Graph mining: Laws, generators, and algorithms (2006) — 738 citations
• Evolutionary clustering (2006) — 716 citations
• Neighborhood formation and anomaly detection in bipartite graphs (2005) — 375 citations
• R-MAT: A Recursive Model for Graph Mining (2004) — 1,267 citations
• Epidemic spreading in real networks: an eigenvalue viewpoint (2003) — 982 citations