Reconstructing propagation networks with temporal similarity metrics

Reconstructing propagation networks with temporal similarity

Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a...

Providing a Link Prediction Model based on Structural and Homophily Similarity in Social Networks

In recent years, with the growing number of online social networks, these networks have become one of the best markets for advertising and commerce, so studying these networks is very important. Most online social networks are growing and changing with new communications (new edges). Forecasting new edges in online social networks can give us a better understanding of the growth of these networ...

TRPC: An Ensembled Online Prediction Mechanism for Trusted Recommender System

The most recent invasions in social networks make it inevitable to develop a network with high dependence and confidence to users. Even though recommender systems of today use advanced parallelism in web development, achieving trustworthiness in such a system has been a challenging task for several years. To overcome the existing sparsity, scalability and dynamism in new item/user issues, we pr...

Planning for Protected Areas Using Spatial and Temporal Metrics

A Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks

Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN). In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN) which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple faul...