Abstract
With the advent of technology, spamming of web pages which is a methodology by which the imposter pages get a higher rank than the true or genuine pages in the search engine’s results to have become prevalent. It poses a gigantic issue for search engines, making it essential for search engines to be able to catch hold and identify spam web pages during crawling. One of the ways in which this issue can be visualized is by considering the underlying web graph structure and the directed URL links(edges) between different spam and true web hosts(nodes), as well as the content attributes of each of the web pages. This graph structure on being fed to an inductive graph neural network model for training purposes will enforce the model to efficiently categorize the new, previously unseen web hosts as web spam or genuine. A GraphSAGE model was developed for carrying out node classification that would leverage the web content attribute data to create node embeddings for the entirely new, unseen web host nodes.
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Kulkarni, A., Solani, D., Sanghavi, P., Kunchapu, A., Vijayalakshmi, M., Nair, S. (2022). Discernment of Unsolicited Internet Spamdexing Using Graph Theory. In: Suma, V., Baig, Z., Kolandapalayam Shanmugam, S., Lorenz, P. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-19-1012-8_4
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DOI: https://doi.org/10.1007/978-981-19-1012-8_4
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