A new stochastic diffusion model for influence maximization in social networks.

Most current studies on information diffusion in online social networks focus on the deterministic aspects of social networks. However, the behavioral parameters of online social networks are uncertain, unpredictable, and time-varying. Thus, deterministic graphs for modeling information diffusion in online social networks are too restrictive to solve most real network problems, such as influence maximization.

Detecting community structure in signed and unsigned social networks by using weighted label propagation.

Detecting community structure is one of the most important problems in analyzing complex networks such as technological, informational, biological, and social networks and has great importance in understanding the operation and organization of these networks. One of the significant properties of social networks is the communication intensity between the users, which has not received much attention so far. Most of the proposed methods for detecting community structure in social networks have only considered communications between users.

Chaotic memetic algorithm and its application for detecting community structure in complex networks.

Community structure is one of the most important topological characteristics of complex networks. Detecting the community structure is a highly challenging problem in analyzing complex networks and it has high significance for understanding the function and organization of complex networks. A wide range of algorithms for this problem uses the maximization of a quality function called modularity.

Wavefront cellular learning automata.

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them.

Irregular Cellular Learning Automata.

Cellular learning automaton (CLA) is a recently introduced model that combines cellular automaton (CA) and learning automaton (LA). The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. This model has been used to solve problems in areas such as channel assignment in cellular networks, call admission control, image processing, and very large scale integration placement.

Cellular learning automata with multiple learning automata in each cell and its applications.

The cellular learning automaton (CLA), which is a combination of cellular automaton (CA) and learning automaton (LA), is introduced recently. This model is superior to CA because of its ability to learn and is also superior to single LA because it is a collection of LAs which can interact with each other. The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA.