Abstract
In this paper, we will apply, to the task of detecting web spam, a combination of the best of its breed algorithms for processing graph domain input data, namely, probability mapping graph self organizing maps and graph neural networks. The two connectionist models are organized into a layered architecture, consisting of a mixture of unsupervised and supervised learning methods. It is found that the results of this layered architecture approach are comparable to the best results obtained so far by others using very different approaches.
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Di Noi, L., Hagenbuchner, M., Scarselli, F., Tsoi, A.C. (2010). Web Spam Detection by Probability Mapping GraphSOMs and Graph Neural Networks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_45
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DOI: https://doi.org/10.1007/978-3-642-15822-3_45
Publisher Name: Springer, Berlin, Heidelberg
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