Science of Success



A network-based normalized impact measure reveals successful periods of scientific discovery across discipline

BY Qing Ke, Alexander J. Gates, and Albert-László Barabási

Who Supports American Art Museums? Introducing a New Dataset and Data Sources about Museum Funding

BY Albert-László Barabási, Louis Shekhtman

Impact of physicality on network structure

BY Márton Pósfai, Balázs Szegedy, Iva Bačić, Luka Blagojević, Miklós Abért, János Kertész, László Lovász & Albert-László Barabási

Non-Coding RNAs Improve the Predictive Power of Network Medicine

BY Deisy Morselli Gysi and Albert-László Barabási

Quantifying hierarchy and prestige in US ballet academies as social predictors of career success

BY Yessica Herrera-Guzmán, Alexander J. Gates, Cristian Candia & Albert-László Barabási

Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine

BY Xiao Gan, Zixin Shu, Xinyan Wang, Dengying Yan, Jun Li, Shany Ofaim, Réka Albert, Xiaodong Li, Baoyan Liu, Xuezhong Zhou, and Albert-lászló Barabási

Philanthropy in art: locality, donor retention, and prestige

BY Louis Michael Shekhtman & Albert-László Barabási

Machine learning prediction of the degree of food processing

BY Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian & Albert-László Barabási

Improving the generalizability of protein-ligand binding predictions with AI-Bind

BY Ayan Chatterjee, Robin Walters, Zohair Shafi, Omair Shafi Ahmed, Michael Sebek, Deisy Gysi, Rose Yu, Tina Eliassi-Rad, Albert-László Barabási, & Giulia Menichetti.

Accelerating network layouts using graph neural networks

BY Csaba Both, Nima Dehmamy, Rose Yu & Albert-László Barabási

Genomics and phenomics of body mass index reveals a complex disease network

BY Huang, J., Huffman, JE., Huang, Y., Do Valle, I., Assimes, TL., Raghavan, S., Voight, B.F., Liu, C., Barabasi, A.-L., Huang, RDL., Hui, Q., Nguyen, X-M T., Ho, Y.-L., Djousse, L., Lynch, J.A., Vujkovic, M., Techeandjiue, C., Tang, H., Damrauer, SM., Reaven, P.D., Miller, D., Phillips, L.S. Ng, MCY. Graff, M., Haiman, C.A., Loos, RJF., North, KE., Yengo, L., Smith, GD., Saleheen, D., GAziano, JM., Rader, DJ., Tsao, PS., Cho, K., Change, K-M., Wilson, PWF., VA Million Veteran Program, Sun Y.V., O’Donnel, CJ.

Non-Coding RNAs Improve the Predictive Power of Network Medicine

BY Deisy Morselli Gysi, Albert-Laszlo Barabasi




CCNR/The Lab: The Center for Complex Network Research (CCNR), directed by Professor Albert-László Barabási, has a simple objective: think networks. The center's research focuses on how networks emerge, what they look like, and how they evolve; and how networks impact on understanding of complex systems. To understand networks, CCNR's research has developed to rather unexpected areas. Certain studies include the topology of the www - showing that webpages are on average 19 clicks form each other; complex cellular network inside the cell-looking at both metabolic and genetic networks; the Internet's Achilles' Heel. The center's researchers have even ventured to study how actors are connected in Hollywood.