Predict the user s position within the communities of social networks

Predict the user s position within the communities of social networks

A social network is a set of organizations or individuals that make up the nodes.Today, social networks are extremely popular, Because the communication channels between individuals are very inexpensive communication. One of the important questions in relation to social networks, finding influential nodes. Influential nodes are nodes that are indicators of centralization rather than other nodes. In this thesis, we use of closeness centrality ,betwenness centrality , eigenvector centrality page rank. Given the importance of these parameters for a node, it seems necessary to fit through a prediction model to estimate these indicators deal, In the absence of actual values can be estimated through an index to specify very close to reality. Artificial Neural Networks novel computational methods for machine learning, knowledge representation, and finally apply the knowledge gained to predict the response of the output of the system are complex. This high performance networks for estimation and approximation. In this thesis Artificial Neural Network Multilayer Perceptron (MLP) and radial circuit function network (RBF) and apply them to the collection metrics used to estimate the dataset Zachary, And then by comparing the two methods, the accuracy of prediction realized by multilayer neural networkAnd we can be in determining the parameters (such as political community gathering place for members of society, which plays an important role in the importance of a social network) is used.

A social network is a set of organizations or individuals that make up the nodes.Today, social networks are extremely popular, Because the communication channels between individuals are very inexpensive communication. One of the important questions in relation to social networks, finding influential nodes. Influential nodes are nodes that are indicators of centralization rather than other nodes. In this thesis, we use of closeness centrality ,betwenness centrality , eigenvector centrality page rank. Given the importance of these parameters for a node, it seems necessary to fit through a prediction model to estimate these indicators deal, In the absence of actual values can be estimated through an index to specify very close to reality. Artificial Neural Networks novel computational methods for machine learning, knowledge representation, and finally apply the knowledge gained to predict the response of the output of the system are complex. This high performance networks for estimation and approximation. In this thesis Artificial Neural Network Multilayer Perceptron (MLP) and radial circuit function network (RBF) and apply them to the collection metrics used to estimate the dataset Zachary, And then by comparing the two methods, the accuracy of prediction realized by multilayer neural networkAnd we can be in determining the parameters (such as political community gathering place for members of society, which plays an important role in the importance of a social network) is used.