Scale Free Information Systems Networks

Decentralized Interaction Estimators in Large Scale Power Systems with Neural Networks.

Finding important hubs in scale-free gene networks

Several biological networks have been recently found to demonstrate scale-free and smallworld behavior instead of random graph characteristics. It is very important to find the hubs (genes, proteins, metabolites and so on) which dominate the topology of scale-free networks, since those are usually of great biological significance. In this paper, we will develop a general information theory meth...

Performance and Robustness of Cellular Automata Computation on Irregular Networks

We investigate the performances of collective task-solving capabilities and the robustness of complex networks of automata using the density and synchronization problems as typical cases. We show by computer simulations that evolved Watts–Strogatz smallworld networks have superior performance with respect to several kinds of scale-free graphs. In addition, we show that Watts–Strogatz networks a...

Merging Similarity and Trust Based Social Networks to Enhance the Accuracy of Trust-Aware Recommender Systems

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

Scale Free Networks; A Literature Review

The Scale-Free Networks model is considered a significant discovery because it has been successfully applied to many complex real-world networks and proved valid. The successful application of the recently discovered model deemed the other model, the random network model, questionable. The presence of scale-free emerging properties in many “real-world” networks provides initial evidence that th...