Node classification over bipartite graphs through projection

• Published in: Machine learning Vol. 110; no. 1; pp. 37 - 87
• Main Authors: Stankova, Marija, Praet, Stiene, Martens, David, Provost, Foster
• Format: Journal Article
• Language: English
• Published: New York Springer US 2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Classification; Computer Science; Control; Data analysis; Datasets; Empirical analysis; Graph theory; Machine Learning; Mechatronics; Natural Language Processing (NLP); Nodes; Performance prediction; Robotics; Simulation and Modeling; Two-mode networks; Behavioral data; Node classification; Bipartite graphs
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-020-05898-0

Many real-world large datasets correspond to bipartite graph data settings—think for example of users rating movies or people visiting locations. Although there has been some prior work on data analysis with such bigraphs, no general network-oriented methodology has been proposed yet to perform node classification. In this paper we propose a three-stage classification framework that effectively deals with the typical very large size of such datasets. The stages are: (1) top node weighting, (2) projection to a weighted unigraph, and (3) application of a relational classifier. This paper has two major contributions. Firstly, this general framework allows us to explore the design space, by applying different choices at the three stages, introducing new alternatives and mixing-and-matching to create new techniques. We present an empirical study of the predictive and run-time performances for different combinations of functions in the three stages over a large collection of bipartite datasets with sizes of up to 20 million × 30 million nodes. Secondly, thinking of classification on bigraph data in terms of the three-stage framework opens up the design space of possible solutions, where existing and novel functions can be mixed and matched, and tailored to the problem at hand. Indeed, in this work a novel, fast, accurate and comprehensible method emerges, called the SW-transformation, as one of the best-performing combinations in the empirical study.