Machine intelligence and network science for complex systems big data analysis

主讲人 Speaker:Carlo Vittorio Cannistraci (Tsinghua University)
时间 Time:Fri., 4:00-5:00 pm, Mar. 24, 2023
地点 Venue:Lecture Hall, Floor 3, Jin Chun Yuan West Bldg.;Zoom Meeting ID: 271 534 5558 Passcode: YMSC


I will present our research at the Center for Complex Network Intelligence (CCNI) that I recently established in the Tsinghua Laboratory of Brain and Intelligence at the Tsinghua University in Beijing. We adopt a transdisciplinary approach integrating information theory, machine learning and network science to investigate the physics of adaptive complex networked systems at different scales, from molecules to ecological and social systems, with a particular attention to biology and medicine, and a new emerging interest for the analysis of complex big data in social and economic science. Our theoretical effort is to translate advanced mathematical paradigms typically adopted in theoretical physics (such as topology, network and manifold theory) to characterize many-body interactions in complex systems. We apply the theoretical frameworks we invent in the mission to develop computational tools for machine intelligent systems and network analysis. We deal with: prediction of wiring in networks, sparse deep learning, network geometry and multiscale-combinatorial marker design for quantification of topological modifications in complex networks. This talk will focus on two main theoretical innovation. Firstly, the development of machine learning and computational solutions for network geometry, topological estimation of nonlinear relations in high-dimensional data1 (or in complex networks2) and its relevance for applications in big data3, with a particular emphasis on brain connectome analysis4. Secondly, we will discuss the Local Community Paradigm (LCP)5,6 and its recent extension to the Cannistraci-Hebb network automata, which are brain-inspired theories proposed to model local-topology-dependent link-growth in complex networks and therefore are useful to devise topological methods for link prediction in sparse deep learning, or monopartite5 and bipartite6 networks, such as molecular drug-target interactions7 and product-consumer networks.


Carlo Vittorio Cannistraci is a theoretical engineer, Zhou Yahui Chair Professor, Chief Scientist at the Tsinghua Laboratory of Brain and Intelligence (THBI), Director of the Center for Complex Network Intelligence (CCNI) at THBI and member of the Department of Computer Science, Department of Physics and Department of Biomedical Engineering at the Tsinghua University, Beijing, China. Carlo’s area of research embraces information theory, machine learning and physics of complex systems and networks including also applications in systems biomedicine and neuroscience. Nature Biotechnology selected Carlo’s article (Cell 2010)8 on machine learning in developmental biology to be nominated in the list of 2010 notable breakthroughs in computational biology. Circulation Research featured Carlo’s work (Circulation Research 2012)9 on leveraging a cardiovascular systems biology strategy to predict future outcomes in heart attacks, commenting: “a space-aged evaluation using computational biology”. The Technical University Dresden honoured Carlo of the Young Investigator Award 2016 in Physics for his work on the local-community-paradigm theory and link prediction in monopartite5 and bipartite networks6. In 2017, Springer-Nature scientific blog highlighted with an interview to Carlo his study on “How the brain handles pain through the lens of network science”10. The American Heart Association covered on its website the chronobiology discovery of Carlo on how the sunshine affects the risk and time onset of heart attack11. In 2018, Nature Communications featured Carlo’s article entitled “Machine learning meets complex networks via coalescent embedding in the hyperbolic space”3 in the selected interdisciplinary collection of recent research on complex systems. In 2019, Scientific Reports selected Carlo’s interview between all their Editors to represent the journal in the social media. In 2019, Carlo won the Shanghai 1000 talents plan award, sponsored by CAS-MPG Partner Institute for Computational Biology. In 2020, Carlo was awarded of the Zhou Yahui Chair Professorship of Tsinghua University. In 2021, Carlo’s won the National high-level talent program award from the Minister of Science of China.


1. Cannistraci, C. V., Ravasi, T., Montevecchi, F. M., Ideker, T. & Alessio, M. Nonlinear dimension reduction and clustering by minimum curvilinearity unfold neuropathic pain and tissue embryological classes. Bioinformatics 26, i531–i539 (2010).

2. Cannistraci, C. V., Alanis-Lobato, G. & Ravasi, T. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding. in Bioinformatics 29, (2013).

3. Muscoloni, A., Thomas, J. M., Ciucci, S., Bianconi, G. & Cannistraci, C. V. Machine learning meets complex networks via coalescent embedding in the hyperbolic space. Nat. Commun. 8, 1615 (2017).

4. Cacciola, A. et al. Coalescent embedding in the hyperbolic space unsupervisedly discloses the hidden geometry of the brain. (2017).

5. Cannistraci, C. V., Alanis-Lobato, G. & Ravasi, T. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci. Rep. 3, 1–13 (2013).

6. Daminelli, S., Thomas, J. M., Durán, C. & Vittorio Cannistraci, C. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks. New J. Phys. 17, 113037 (2015).

7. Durán, C. et al. Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory. Brief. Bioinform. 8, 3–62 (2017).

8. Ravasi, T. et al. An atlas of combinatorial transcriptional regulation in mouse and man. Cell 140, 744–52 (2010).

9. Ammirati, E. et al. Identification and predictive value of interleukin-6+ interleukin-10+ and interleukin-6-interleukin-10+ cytokine patterns in st-elevation acute myocardial infarction. Circ. Res. 111, 1336–1348 (2012).

10. Narula, V., Zippo, A. G., Muscoloni, A., Biella, G. E. M. & Cannistraci, C. V. Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain? Appl. Netw. Sci. 2, 28 (2017).

11. Cannistraci, C. V. et al. “Summer Shift”: A Potential Effect of Sunshine on the Time Onset of ST‐Elevation Acute Myocardial Infarction. J. Am. Heart Assoc. 7, e006878 (2018).