Much current research is concentrated on building computing networks based on nanoscale devices. However, there has been less progress in programming these networks to produce useful computation. This paper considers a model of computation based on Random Boolean Networks (RBNs). RBNs have the ability to form complex dynamic patterns in ways that produce collective information processing. This ...

Most existing approaches to identity federation are based on static relationships. This leads to problems with scalability and deployment in real-time environment such as mobile networks. This paper introduces an underlying network and trust model for dynamic federation. We present a modified Dijkstra algorithm to calculate the trust value and apply a distributed reputation calculated based on ...

Social networks have a profound influence on opinion formation, and at the same time opinion similarity and diversity can draw individuals together or drive them apart. In light of this, we propose a dynamic model in which individuals, belonging to 2 different groups, sequentially update both their opinions and their connections. We show that with probability one this dynamic process converges ...

We consider a dynamic social network model in which agents play repeated games in pairings determined by a stochastically evolving social network. Individual agents begin to interact at random, with the interactions modeled as games. The game payoffs determine which interactions are reinforced, and the network structure emerges as a consequence of the dynamics of the agents' learning behavior. ...

In this paper, a dynamic network model based on agent behavior is introduced to explain the formationmechanismof interbankmarket network.We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network an...

A polymorphic dynamic network loading (PDNL) model is developed and discretized to integrate a variety of macroscopic traffic flow and node models. The polymorphism, realized through a general node-link interface and proper discretization, offers several prominent advantages. First of all, PDNL allows road facilities in the same network to be represented by different traffic flow models based o...

Graph Properties and Retrieval We investigate the information that is contained in the structure of a topology preserving neural network. We consider a topological map as a graph G, propose certain properties of the structure and formulate the respective expectable results of network interpretation. The scenario we deal with is the nearest-neighbor approach to classification. The problems are t...

Developing realistic models for the evaluation of sustainable supply chains has turned into a major challenge facing managers. The decision-making approaches proposed here consist of two stages. At the first stage, a dynamic-network data envelopment analysis (DNDEA) model is established for the first time, wherein the current efficiency of a business can be influenced by its prior social and en...

Hidden Markov Models (HMM) are currently widely used in Automatic Speech Recognition (ASR) as being the most effective models. In addition, the HMM are just a special case of graphical models which are dynamic Bayesian Networks (DBN). These are modeling tools more sophisticated because they allow to include several specific variables in the problem of automatic speech recognition other than the...