Graph Neural Network (GNN)
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities. …

Leave-p-Out Cross Validation (LpOCV)
As the name suggests, leave-p-out cross-validation (LpO CV) involves using p observations as the validation set and the remaining observations as the training set. This is repeated on all ways to cut the original sample on a validation set of p’ observations and a training set. LpO cross-validation requires to learn and validate times (where n is the number of observation in the original sample). So as soon as n is quite big it becomes impossible to calculate. …

Apache Tika
The Apache Tika toolkit detects and extracts metadata and text content from various documents – from PPT to CSV to PDF – using existing parser libraries. Tika unifies these parsers under a single interface to allow you to easily parse over a thousand different file types. Tika is useful for search engine indexing, content analysis, translation, and much more. …