Transfer Learning for Node Regression Applied to
Spreading Prediction
Sebastian Mežnar
Jožef Stefan Institute, Jamova 39, Ljubljana, Slovenia
Nada Lavrač
Jožef Stefan Institute, Jamova 39, Ljubljana, Slovenia
and
University of Nova Gorica, Glavni trg 8, Vipava, Slovenia
Blaž Škrlj
Jožef Stefan Institute
and
Jožef Stefan International Postgraduate School
Jamova 39, Ljubljana, Slovenia
Abstract
Understanding how information propagates in real-life complex networks yields a better understanding of dynamic processes such as misinformation or epidemic spreading. The recently introduced branch of machine learning methods for learning node representations offers many novel applications, one of them being the task of spreading prediction addressed in this paper. We explore the utility of the state-of-the-art node representation learners when used to assess the effects of spreading from a given node, estimated via extensive simulations. Further, as many real-life networks are topologically similar, we systematically investigate whether the learned models generalize to previously unseen networks, showing that in some cases very good model transfer can be obtained. This paper is one of the first to explore transferability of the learned representations for the task of node regression; we show there exist pairs of networks with similar structure between which the trained models can be transferred (zero-shot) and demonstrate their competitive performance. To our knowledge, this is one of the first attempts to evaluate the utility of zero-shot transfer for the task of node regression.
Keywords: epidemics; neural networks; machine learning; spreading; transfer learning
Cite this publication as:
S. Mežnar, N. Lavrač and B. Škrlj, "Transfer Learning for Node Regression Applied to Spreading Prediction," Complex Systems, 30(4), 2021 pp. 457–481.
https://doi.org/10.25088/ComplexSystems.30.4.457
