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Graphs are one of the most efficacious structures for representing datapointsand their relations, and they have been largely exploited for differentapplications. Previously, the higher-order relations between the nodes havebeen modeled by a generalization of graphs known as hypergraphs. Inhypergraphs, the edges are defined by a set of nodes i.e., hyperedges todemonstrate the higher order relationships between the data. However, there isno explicit higher-order generalization for nodes themselves. In this work, weintroduce a novel generalization of graphs i.e., K-Nearest Hyperplanes graph(KNH) where the nodes are defined by higher order Euclidean subspaces formulti-view modeling of the nodes. In fact, in KNH, nodes are hyperplanes ormore precisely m-flats instead of datapoints. We experimentally evaluate theKNH graph on two multi-aspect datasets for misinformation detection. Theexperimental results suggest that multi-view modeling of articles using KNHgraph outperforms the classic KNN graph in terms of classification performance.

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