Detecting the Authors of Texts by Neural Network Committee Machines

This paper proposes a means of using a boosting by filtering algorithm in artificial neural networks to identify the author of a text. This approach involves filtering the training examples by different versions of a weak learning algorithm. It assures the availability of a large source of examples, with the examples being either discarded or kept during training. An advantage of this approach is that it allows for a small memory requirement. Once the network has been trained, its hidden layer activations are recorded as a representation of the selected lexical descriptors of an author. This stored information can then be used to identify the texts written by the same author. Texts studied are literary works of two Bosnian writers, Ivo Andrić  (1892-1975) and M. Meša Selimović (1910-1982). The data collected by counting syntactic characteristics in 1466 paragraphs of "na drini ćupria" by Ivo Andrić, and "derviš i smirt"  by M. Meša Selimović each

Structural Micro Forces in Flickr Social Network

Previous studies on network structure of large online social networks focused almost exclusively on exploring the global network descriptive statistics. Few of the studies that have researched how these networks evolve from the micro forces at the local level have fallen short of modeling reciprocity and different ways triangle closure. By focusing on the denser areas of the Flickr network (user groups) and with the help of recently extended biased net modeling framework, we specified and fitted models and estimated parameters for all possible purely structural dyadic and triadic network effects: reciprocity, transitivity, structural similarity, closure and cyclicality. Our results showed that the reciprocity is by far the most strongest force acting in the network, followed by transitivity, closure and structural similarity. Cyclicality has been, surprisingly, proven not to exist at all. Furthermore, we have found that the size of the groups corresponds negatively with the magnitude of each of the micro forces. Keywords: Flickr, online networks, network structure, online social network