@article {Bianchini2016283, title = {A comparative study of inductive and transductive learning with feedforward neural networks}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {10037 LNAI}, year = {2016}, note = {cited By 0}, pages = {283-293}, abstract = {Traditional supervised approaches realize an inductive learning process: A model is learnt from labeled examples, in order to predict the labels of unseen examples. On the other hand, transductive learning is less ambitious. It can be thought as a procedure to learn the labels on a training set, while, simultaneously, trying to guess the best labels on the test set. Intuitively, transductive learning has the advantage of being able to directly use training patterns while deciding on a test pattern. Thus, transductive learning faces a simpler problem with respect to inductive learning. In this paper, we propose a preliminary comparative study between a simple transductive model and a pure inductive model, where the learning architectures are based on feedforward neural networks. The goal is to understand how transductive learning affects the complexity (measured by the number of hidden neurons) of the exploited neural networks. Preliminary experimental results are reported on the classical two spirals problem. {\textcopyright} Springer International Publishing AG 2016.}, doi = {10.1007/978-3-319-49130-1_21}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006009614\&doi=10.1007\%2f978-3-319-49130-1_21\&partnerID=40\&md5=0072365bc5774e0eec549d8f61a2ddc4}, author = {Bianchini, M.a and Belahcen, A.b and Scarselli, F.a} } @article {Belahcen201583, title = {Web spam detection using transductive-inductive graph neural networks}, journal = {Smart Innovation, Systems and Technologies}, volume = {37}, year = {2015}, note = {cited By 0}, pages = {83-91}, abstract = {The Web spam detection problem has received a growing interest in the last few years, since it has a considerable impact on search engine reputations, being fundamental for the increase or the deterioration of the quality of their results. As a matter of fact, the World Wide Web is naturally represented as a graph, where nodes correspond to Web pages and edges stand for hyperlinks. In this paper, we address the Web spam detection problem by using the GNN architecture, a supervised neural network model capable of solving classification and regression problems on graphical domains. Interestingly, a GNN can act as a mixed transductive-inductive model that, during the test phase, is able to classify pages by using both the explicit memory of the classes assigned to the training examples, and the information stored in the network parameters. In this paper, this property of GNNs is evaluated on a well-known benchmark for Web spam detection, the WEBSPAM-UK2006 dataset. The obtained results are comparable to the state-of-the-art on this dataset. Moreover, the experiments show that performances of both the standard and the transductive-inductive GNNs are very similar, whereas the computation time required by the latter is significantly shorter. {\textcopyright} Springer International Publishing Switzerland 2015}, doi = {10.1007/978-3-319-18164-6_9}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930960218\&doi=10.1007\%2f978-3-319-18164-6_9\&partnerID=40\&md5=e1f9c9b044f8e3b2f28acf473136ff7a}, author = {Belahcen, A.a b and Bianchini, M.a and Scarselli, F.a} }