Evolving Networks and Collective Behaviour: Analysis, Algoritm and Applications

Network-type’ problems (for example, internet reliability and security, genetic regulation, transportation, utilities and communication infrastructure) are one of the emergent themes of 21st century science. Despite these diverse fields of application, and the varied methods of study used across these different fields, it has become increasingly clear that substantial scientific progress can be made by considering commonalities both through mathematical abstraction, using notation and ideas from graph theory and statistical physics, and through quantitative study of datasets. 

Collective behaviour problems arise in a similarly broad range of situations: obvious examples from the natural world include human crowd dynamics, animal social groups, herding, swarming and shoaling behaviours. By analogy these descriptions have crossed into many other fields, notably as descriptions of behaviour in economics and financial markets. ‘Network-type’ and collective behaviour problems exhibit similarities, for example both tend to be concerned with the emergence of overall behaviour on a system-wide scale from interactions at the level of individuals, where each individual directly influences only a subset of other individuals. It is therefore natural to consider these apparently different topics under the same theme and to initiate discussions and collaborations between researchers across both areas. 

In this workshop, we will invite excellent specialists in applied mathematics, numerical computing and graph theory to exchange their ideas, communicate the latest research results and develop further collaborations. We expect that after the workshop all participants will have a better understanding of the state-of-the-arts of the area and will have gained impetus to further investigate important problems in the area as well as new problems that will certainly come out of the workshop. Topics included but not limited to the following areas: Social network model in modularity and community detection; Random network models, generation and simulation; Dynamics on networks, such as temporal networks; Open problems in Graph theory and algorithms.