Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
ACM Transactions on Internet Technology (TOIT)
Know your neighbors: web spam detection using the web topology
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Introduction to Information Retrieval
Introduction to Information Retrieval
The graph neural network model
IEEE Transactions on Neural Networks
Computational capabilities of graph neural networks
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
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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.