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ISSN 1556-6757 |
SJI |
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Volume
2, Issue 1, 2010
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Abstract
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
Tarik Rashid
Abstract
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
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