Konferenzen  >  Mathematik  >  Komplexe Netzwerke

Simons Laufer Mathematical Sciences Institute (SLMath)

In a growing number of applications, one needs to analyze and interpret data coming from massive networks. The statistical problems arising from such applications lead to important mathematical challenges: building novel probabilistic models, understanding the possibilities and limitations for statistical detection and inference, designing efficient algorithms, and understanding the inherent limitations of fast algorithms. The workshop will bring together leading researchers in combinatorial statistics, machine learning, and random graphs in the hope of cross-fertilization of ideas.

combinatorial statistics, random graphs, network inference, network reconstruction, detection, estimation

Wissenschaftliches Rechnen und Datenwissenschaft,    Graphentheorie und Kombinatorik

DySyX'2025 is a two days workshop organized by Faulty of Sciences of Kénitra (LAGA), Ibn Tofail University and Le Havre Normandie University (LMAH). It will focus on recent advances in dynamical systems, complex networks, and applications in some fields of science and engineering. This meeting will provide a joint forum for leading applied mathematicians and scientists actively working in the diverse fields of life sciences in order to exchange ideas, advance the knowledge of research, discuss future research challenges, and explore opportunities for collaborative exchange and education. There will be several guest talks covering such recent trends.

Email: dysyx2025@gmail.com

Theories, Concepts and Méthodologies Nonlinear Dynamical Systems and Modelling ODE, Delay DE, Stochastics DE, Stochastics DDE, Statistics, PDE, Delay PDE, Stochastics PDE, Complex networks, Complex Systems, Synchronization Bifurcations, Chaos. Applications Biological Networks Bio-Systems & Bio-Mathematics Ecology, Environmental Sciences and Ecosystems Epidemiology & Eco-Epidemiology Neuroscience

Angewandte Mathematik (allgem.),    Komplexe Systeme und Chaos

Virtually every human-technology interaction, or sensor network, generates observations that are in some relation with each other. As a result, many data science problems can be viewed as a study of some properties of complex networks in which nodes represent the entities that are being studied and edges represent relations between these entities. Such networks are often large-scale, decentralized, and evolve dynamically over time. Modeling and mining complex networks in order to understand the principles governing the organization and the behaviour of such networks is crucial for a broad range of fields of study, including information and social sciences, economics, biology, and neuroscience. The aim of the 20th Workshop on Modelling and Mining Networks (WAW 2025) is to further the understanding of networks that arise in theoretical as well as applied domains. The goal is also to stimulate the development of high-performance and scalable algorithms that exploit these networks. The workshop welcomes the researchers who are working on graph-theoretic and algorithmic aspects of networks represented as graphs or hypergraphs and other higher order structures.

Simulation,    Algorithmen und Datenstrukturen

Banach Center

This meeting aims to gather top-notch specialists in the field of dynamical systems on networks to discuss differences and similarities in the approaches based on optimal transport, gradient flows, and operator theory. At the conference, the most recent topics such as co-evolving networks, multi-scale analysis, control theory, and applications will also be addressed.