Machine intelligence and network science for complex systems big data analysis
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© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
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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 wi...ring 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 data
(or in complex networks) and its relevance for applications in big data, with a emphasis on
brain connectome analysis. Secondly, we will discuss the Local Community Paradigm (LCP)
and its recent extension to the Cannistraci-Hebb network automata, which are braininspired
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 monopartite and bipartite networks, such as molecular drugtarget
interactions and product-consumer networks.
Keywords:
network topology and geometry / network automata / network biology / network neuroscience / artificial intelligenceSource:
4th Belgrade Bioinformatics Conference, 2023, 4, 3-3Publisher:
- Belgrade : Institute of molecular genetics and genetic
Note:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
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Institution/Community
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Vittorio Cannistraci, Carlo PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1938 AB - 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 data (or in complex networks) and its relevance for applications in big data, with a emphasis on brain connectome analysis. Secondly, we will discuss the Local Community Paradigm (LCP) and its recent extension to the Cannistraci-Hebb network automata, which are braininspired 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 monopartite and bipartite networks, such as molecular drugtarget interactions and product-consumer networks. PB - Belgrade : Institute of molecular genetics and genetic C3 - 4th Belgrade Bioinformatics Conference T1 - Machine intelligence and network science for complex systems big data analysis EP - 3 SP - 3 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_1938 ER -
@conference{ author = "Vittorio Cannistraci, Carlo", year = "2023", abstract = "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 data (or in complex networks) and its relevance for applications in big data, with a emphasis on brain connectome analysis. Secondly, we will discuss the Local Community Paradigm (LCP) and its recent extension to the Cannistraci-Hebb network automata, which are braininspired 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 monopartite and bipartite networks, such as molecular drugtarget interactions and product-consumer networks.", publisher = "Belgrade : Institute of molecular genetics and genetic", journal = "4th Belgrade Bioinformatics Conference", title = "Machine intelligence and network science for complex systems big data analysis", pages = "3-3", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_1938" }
Vittorio Cannistraci, C.. (2023). Machine intelligence and network science for complex systems big data analysis. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic., 4, 3-3. https://hdl.handle.net/21.15107/rcub_imagine_1938
Vittorio Cannistraci C. Machine intelligence and network science for complex systems big data analysis. in 4th Belgrade Bioinformatics Conference. 2023;4:3-3. https://hdl.handle.net/21.15107/rcub_imagine_1938 .
Vittorio Cannistraci, Carlo, "Machine intelligence and network science for complex systems big data analysis" in 4th Belgrade Bioinformatics Conference, 4 (2023):3-3, https://hdl.handle.net/21.15107/rcub_imagine_1938 .
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