  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.
