SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction Identification.

The identification of drug-target interactions (DTI) is critical for drug discovery and repositioning, as it reveals potential therapeutic uses of existing drugs, accelerating development and reducing costs. However, most existing models focus only on direct similarity in homogeneous graphs, failing to exploit the rich similarity in heterogeneous graphs. To address this gap, inspired by real-world social interaction behaviors, we propose SOC-DGL, which comprises two specialized modules: the Affinity-Driven Graph Learning (ADGL) module, learning global similarity through an affinity-enhanced drug-target graph, and the Equilibrium-Driven Graph Learning (EDGL) module, capturing higher-order similarity by amplifying the influence of even-hop neighbors using an even-polynomial graph filter based on balance theory. This dual approach enables SOC-DGL to effectively capture similarity information across multiple interaction scales within affinity and association matrices. To address the issue of imbalance in DTI datasets, we propose an adjustable Reweighted Loss Function (RLF) that moderates the positive-class compensation weight in weighted binary cross-entropy through an adjustable parameter. Experiments on four benchmark datasets show that SOC-DGL achieves competitive performance under both balanced and imbalanced settings. Moreover, SOC-DGL predicts the top 9 drugs known to bind ABL1, and further analyzes the 10th-ranked drug, which has not been experimentally confirmed to interact with ABL1, providing supporting evidence for its potential binding. The source code is available for academic purposes at https://github.com/Zhaoxiang0422/SOC-DGL.