ISSN 1556-6757












Volume 2, Issue 1, 2010


Evolving Graph Representation and Visualization
Anurat Chapanon, Mukkai Krishnamoort, G. M. Prabhu, John Puni


The study of evolution of networks has received increased interest with the recent discovery that many real-world networks possess many things in common, in particular the manner of evolution of such networks. By adding a dimension of time to graph analysis, evolving graphs present opportunities and challenges to extract valuable information. This paper introduces the Evolving Graph Markup Language (EGML), an XML application for representing evolving

graphs and related results. Along with EGML, a software tool is provided for the study of evolving graphs. New evolving graph drawing techniques based on the force-directed graph layout algorithm are also explored. These evolving graph techniques reduce vertex movements between graph instances, so that an evolving graph can be viewed with smooth transitions.
Full Article



Shape Recognition through an Alternative Recurrent Network Architecture

Tarik Rashid


This paper focuses on a study of different Recurrent Neural Network architectures. A new alternative type of recurrent neural network is introduced. This alternative recurrent neural network is based on the simple recurrent network (SRN) but has an architecture that consists of fully interconnected layers. The network has feedback connections from the input, hidden, and output layers, with each connecting to its own designated context layer. A mathematical model for the network is detailed in the paper. The new alternative recurrent neural network, developed for recognition tasks, demonstrates superior performance over simple recurrent networks in recognition and prediction tasks.  Full Article