KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection

Abstract: Graphs are one of the most efficacious structures for representing datapoints and their relations, and they have been largely exploited for different applications. Previously, the higher-order relations between the nodes have been modeled by a generalization of graphs known as hypergraphs. In hypergraphs, the edges are defined by a set of nodes i.e., hyperedges to demonstrate the higher order relationships between the data. However, there is no explicit higher-order generalization for nodes themselves. In this work, we introduce a novel generalization of graphs i.e., K-Nearest Hyperplanes graph (KNH) where the nodes are defined by higher order Euclidean subspaces for multi-view modeling of the nodes. In fact, in KNH, nodes are hyperplanes or more precisely m-flats instead of datapoints. We experimentally evaluate the KNH graph on two multi-aspect datasets for misinformation detection. The experimental results suggest that multi-view modeling of articles using KNH graph outperforms the classic KNN graph in terms of classification performance.