Comparison of Centralities for Biological Networks

Vincent - Visualization of Network Centralities

The use of network centralities in the field of network analysis plays an important role when the relative importance of nodes within the network topology should be rated. A single network can easily be represented by the use of standard graph drawing algorithms, but not only the exploration of one centrality might be important: the comparison of two or more of them is often crucial for a bette...

Comparison of MLP NN Approach with PCA and ICA for Extraction of Hidden Regulatory Signals in Biological Networks

The biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. So far, many computational and statistical methods such as PCA and ICA have been employed for computing low-dimensional or hidden represe...

CentiBiN Centralities in Biological Networks

Centralities Based Analysis of Complex Networks

Characterizing, describing, and extracting information from a network is by now one of the main goals of science, since the study of network currently draws the attention of several fields of research, as biology, economics, social science, computer science and so on. The main goal is to analyze networks in order to extract their emergent properties (Bhalla & Iyengar (1999)) and to understand f...

Exponentially Twisted Sampling: a Unified Approach for Centrality Analysis in Attributed Networks

In our recent works, we developed a probabilistic framework for structural analysis in undirected networks and directed networks. The key idea of that framework is to sample a network by a symmetric and asymmetric bivariate distribution and then use that bivariate distribution to formerly defining various notions, including centrality, relative centrality, community, and modularity. The main ob...