BCGL: Binary Classification-Based Graph Layout

Graph layouts reveal global or local structures of graph data. However, there are few studies on assisting readers in better reconstructing a graph from a layout. This paper attempts to generate a layout whose edges can be reestablished. We reformulate the graph layout problem as an edge classification problem. The inputs are the vertex pairs, and the outputs are the edge existences. The trainable parameters are the laidout coordinates of the vertices. We propose a binary classification-based graph layout (BCGL) framework in this paper. This layout aims to preserve the local structure of the graph and does not require the total similarity relationships of the vertices. We implement two concrete algorithms under the BCGL framework, evaluate our approach on a wide variety of datasets, and draw comparisons with several other methods. The evaluations verify the ability of the BCGL in local neighborhood preservation and its visual quality with some classic metrics.