FUJIWARA LAB SEMINAR

Seminar

2022/10/14, 16:00 p.m. - 17:00 p.m. @ 情報科学研究科棟4階412室

Speaker: Masaki Ochi (Institute of Industrial Science, the University of Tokyo)

Title: TBA

Related Paper: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.4.033129

Seminar

2022/01/26 @ 09:00 a.m. - 12:00 p.m.

Speaker: Dr. Mengqiao Xu (Dalian University of Technology)

Title: Community structure analysis and its application to global maritime transportation networks

Related Paper: https://www.nature.com/articles/s41467-020-16619-5/

※ Please contact Naoya Fujiwara if you want to attend the seminar.

Seminar

2021/09/02 @ 15:00 p.m. - 15:50 p.m.

Speaker: Dr. Yuka Fujiki (AIMR, Tohoku Univ.)

Title: Fractal scale-free networks: real-world networks and a generalized model

Abstract: One of the common statistical properties of complex networks in the real world is the scale-free property, i.e., the degree distribution is approximated by a power-law function. Scale-free networks can be divided into fractal networks or non-fractal networks based on the relationship between the distance between nodes and the number of nodes. While most scale-free networks are non-fractal, some of them in engineering and biological fields, including WWW and metabolic networks, are known to be fractal. Fractal networks have invariance of their structure to renormalization and exhibit various properties not seen in non-fractal networks, such as the appearance of critical phases in percolation on the network anomalous diffusion phenomena. Therefore, mathematical models are needed to understand the structural and dynamical properties of scale-free fractal networks (SFNs). In this study, we generalize the existing deterministic SFN models, which can reproduce various structural features of SFNs in the real world. Since this model generates networks in a deterministic way, we can analytically calculate various properties of the network. This presentation shows the calculation method and results of the exponent of the power-law degree distribution, fractal dimension, average clustering coefficient, global clustering coefficient, and joint probability describing the nearest-neighbor degree correlation. We also derive the critical points and critical exponents of percolation transitions on deterministic SFNs to clarify the relationship between network robustness and structural features of SFNs. Finally, we calculate these percolation properties of bond percolation for several examples of the model. The results show that the critical exponents take different values depending on the cluster coefficients, even for SFNs with the same degree distribution fractal dimension and nearest-neighbor degree correlation.

Further Information: If you have any questions regarding the seminars above, please direct them to Yunhan Du, email du@se.is.tohoku.ac.jp.