The main current work of our group is concerned with applying the theoretical methods that have been developed for the analysis of stochastic fluctuations and complex networks. Developing new algorithms and quantifying the characteristics of stochastic processes, complex networks, and random matrix theory are some of the main challenges in our current research on such complex systems. We are interested in gaining deeper insights about stochastic processes, especially those that are non-stationary, coupled series and identify applications in other fields to which we can apply the methods and expertise. In order to improve our abilities in understanding the nature of the processes, we intend to investigate whether the methods that we have developed can be applied to finance & social networks and network of genes in tumor cells.
- Collective Behavior in Financial Markets, Technology, and Human Networks
- Social Networks Dynamics (Aged networks, Dark, and non-transparent networks)
- Complexity Economy & Econophysics (Crises, Systemic Risk, Portfolio & Horizon Investment)
- Stochastic Processes (Non-Gaussian & several coupled series, Multi-fractal)
- Machine Learning
- Percolation Processes and Structural Phase transitions
- Multilayer Networks
The Twitter team in CCNSD studies Farsi Twitter and its trending topics. Twitter data, in our team, is used as an indicator of the social, cultural and political concerns and interests of Twitter users. We, however, assume that the impact of Twitter, and other social media, does not stop online, and the processes is part of a much larger sphere of influence on Farsi speaking countries and diasporic communities. Currently, we are working on two different projects (1) human-bot interaction in the formation of collective identities in Farsi Twitter and (2) a typology of fake news based on their propagation.
Cancer is commonly known as a disease of the genes and there has been a huge effort to find the effective genes for different cancers. These approaches to control/cure cancer, however, have not been that much successful. In this study, instead of following the prevalent reductionist methods, we have used the approach of Complex Systems. We have inferred regulatory interactions between the genes and by representing each gene as a node and the interaction between each two of them as a link, we have instructed the interaction network. We have seen the pattern of pair-interactions matters for the emergence of collective behaviors from the genes interactions.
We unveil secrets of the financial markets that prove very effective in shaping their future. The question to be answered is why instant high amplitude variations of price returns & trading values are never experienced. We deduce that the key to shedding light on this issue is the quantum potential whose existence is due to the entanglement between a price returns\trading values and their prior-days price returns\trading values. Implementing the quantum potential would enable us to sketch a robust pattern for the price return\trading values fluctuations of the financial markets.
- Financial market images: a practical approach owing to the secret quantum potential, F Tahmasebi, S Meskinimood, A Namaki, SV Farahani, S Jalalzadeh, GR Jafari, EPL (Europhysics Letters) 109 (3), 30001 (2015)
- The impact of trading volume on the stock market credibility: Bohmian quantum potential approach, S. Nasiri, E.Bektas, G.R.Jafari, Physica A (2018)
- Risk Information of Stock Market Using Quantum Potential Constraints, Sina Nasiri, Eralp Bektas, Gholamreza Jafari, 3rd International Conference on Banking and Finance Perspectives (ICBFP-2018), 25-27 April 2018, Famagusta, North Cyprus
Econo & Social Networks
In real social life, each of us has observed how the third persons affect the relationship between two persons. It means the relationship between two persons is only depended on them, but also it is interested in by others. The relationships are balanced if two persons, who are friends, have the same attitude (friendly or unfriendly) toward a third one, otherwise in order to decrease tension, one of them must change his or her attitude or they have to stop their friendship. This concept was introduced for the first time by Heider in the framework of the balance theory. Heider considered triples and defined a triple as unbalanced if the product of its edge signs is negative. Now, this is how it goes; the relation (link) between the members (nodes) i and j is represented by Sij, where if i and j are equal. If the status between two nodes is friendship/enemy, we would have for Sij the values of 1 and −1, respectively. This imposes that for a three node system which shapes a triangle, a balanced triangle is formed only when the product of the values assigned for the links (Sij) between every two nodes has a positive sign. Hence, we would have two balanced and two unbalanced states. Note that the tendency is to have a balanced triangle, which implies that as the network evolves, the unbalanced triangles eventually become balanced….
Evolution of social networks happens simultaneously with the evolution of their links/nodes. However, it is not easy for some links/nodes to evolve in such a process because of their age. In any network, especially real networks could exist that they are not happy with some variations. Understanding stability, aged phase transition, information transformation of social networks are some of my challenges.
In studying the network dynamics of complex systems such as the world-wide-web, genes regulatory networks, and social networks it regularly happens that we do not have access to the complete information about them. Not only the information is incomplete, but also some of the information may be wrong.
So, how can we understand the general or particular behavior of such networks in these scenarios?! In this type of giant networks, a new and very interesting type of uncertainty arises when there is absolutely no guarantee whether a node or a link is visible at all for some parties.
In studying the network dynamics of complex systems such as the world-wide-web, genes regulatory networks, and social networks it regularly happens that we do not have access to the complete information about them. Not only the information is incomplete, but also some of the information may be wrong. So, how can we understand the general or particular behavior of such networks in these scenarios?! In this type of giant networks, a new and very interesting type of uncertainty arises when there is absolutely no guarantee whether a node or a link is visible at all for some parties.
In the real human society, there are two points that we are interested to consider them in the epidemic process. The first is the memory has a great impact on the evolution of every process related to human societies. Among them, the
evolution of an epidemic is directly related to the individuals’ experiences. Indeed, any real epidemic process is clearly sustained by non-Markovian dynamics: memory effects play an essential role in the spreading of diseases.
The second is collective behavior or macroscopic parameters. In social networks, the relationship between two persons can be affected by a third person. The relationships are balanced if two persons, who are friends, have the same attitude (friendly or unfriendly) toward a third one, otherwise in order to decrease tension, one of them must change his or her attitude or they have to stop their friendship.