Web spam pages use various techniques to achieve
higher-than-deserved rankings in a search engine’s
results. While human experts can identify
spam, it is too expensive to manually evaluate a
large number of pages. Instead, we propose techniques
to semi-automatically separate reputable,
good pages from spam. We first select a small set
of seed pages to be evaluated by an expert. Once
we manually identify the reputable seed pages, we
use the link structure of the web to discover other
pages that are likely to be good. In this paper
we discuss possible ways to implement the seed
selection and the discovery of good pages. We
present results of experiments run on the World
Wide Web indexed by AltaVista and evaluate the
performance of our techniques. Our results show
that we can effectively filter out spam from a significant
fraction of the web, based on a good seed
set of less than 200 sites.
Handcock, M.S., Raftery, A.E. and Tantrum, J. (2005).
Model-Based Clustering for Social Networks.
Working Paper no. 46, Center for Statistics and the Social Sciences,
University of Washington.
VisualComplexity.com intends to be a unified resource space for anyone interested in the visualization of complex networks. The project's main goal is to leverage a critical understanding of different visualization methods, across a series of disciplines, as diverse as Biology, Social Networks or the World Wide Web.
Features
* Easy to use Python API
* Flexible data import options
* Scalable
* Built-in reports
* Built-in data export capabilities
* Open source - easily extended
* Fast processing time (efficient use of graph algorithms)
A. Wu, M. Garland, and J. Han. KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, page 719--724. New York, NY, USA, ACM Press, (2004)
J. Kubica, A. Moore, J. Schneider, and Y. Yang. Proceedings of the Eighteenth National Conference on Artificial Intelligence, page 798--804. AAAI Press/MIT Press, (July 2002)
A. Borodin, G. Roberts, J. Rosenthal, and P. Tsaparas. Proceedings of the 10th international conference on World Wide Web, page 415--429. New York, NY, USA, ACM Press, (2001)