title: Solving graph data issues using a layered architecture approach with applications to web spam detection.
abstract: This paper proposes the combination of two state-of-the-art algorithms for processing graph input data, viz., the probabilistic mapping graph self organizing map, an unsupervised learning approach, and the graph neural network, a supervised learning approach. We organize these two algorithms in a cascade architecture containing a probabilistic mapping graph self organizing map, and a graph neural network. We show that this combined approach helps us to limit the long-term dependency problem that exists when training the graph neural network resulting in an overall improvement in performance. This is demonstrated in an application to a benchmark problem requiring the detection of spam in a relatively large set of web sites. It is found that the proposed method produces results which reach the state of the art when compared with some of the best results obtained by others using quite different approaches. A particular strength of our method is its applicability towards any input domain which can be represented as a graph.
authors: University of Siena, Siena, Italy. Electronic address: franco@ing.unisi.it.
authors: Franco
authors: F
authors: Scarselli
authors: Ah Chung
authors: AC
authors: Tsoi
authors: Markus
authors: M
authors: Hagenbuchner
authors: Lucia Di
authors: LD
authors: Noi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics / Q000145:classification*; D003198:Computer Simulation; D003199:Computer Systems; D034742:Electronic Mail / Q000145:classification*; D020407:Internet / Q000145:classification*; D016571:Neural Networks, Computer
keywords: Graph neural network; Layered architecture; Probability Mapping GraphSOM; Web spam detection