Paper published in Machine Learning Journal!

Created in February 04, 2026

2026

We are happy to share that our paper “Select First, Transfer Later: Choosing a Proper Dataset for SRL and GNN Based Transfer Learning” has been published in Machine Learning Journal.

Traditional machine learning models often ignore the relational structure present in many real-world domains. Approaches such as Statistical Relational Learning (SRL) and Graph Neural Networks (GNNs) address this by explicitly modeling dependencies between entities. However, like traditional models, they typically assume that training and testing data come from the same distribution — an assumption that often fails in practice.

Transfer Learning helps by reusing knowledge from one domain in another. But an important question remains largely overlooked:

👉 From where should we transfer?

In this work, we propose a principled method to estimate the suitability of transfer between relational domains using Kullback–Leibler (KL) divergence, computed from a Naive Bayes distribution of the target relational data and each candidate source model (SRL or GNN).

🔎 Main contribution:

  • A strategy to select the most appropriate source domain before performing transfer.
  • Empirical evaluation with state-of-the-art SRL and GNN transfer learning algorithms.
  • Experimental evidence that selecting the right source significantly improves performance.

Our results reinforce that, in relational and graph-based learning, choosing the source domain is just as important as deciding what and how to transfer.