Self-supervised learning (SSL) on graph-structured data has attracted considerable attention recently. Masked graph autoencoder, as one promising generative graph SSL approach that aims to recover masked parts of the input graph data, has shown great success in various downstream graph tasks. However, existing masked graph autoencoders fail to consider the degree of difficulty of recovering the masked edges that often have different impacts on the model performance, resulting in suboptimal node representations. To tackle this challenge, in this paper, we propose a novel curriculum based self-supervised masked graph autoencoder that is able to capture and leverage the underlying degree of difficulty of data dependencies hidden in edges, and design better mask-reconstruction pretext tasks for learning informative node representations. Specifically, we first design a difficulty measurer to identify the underlying structural degree of difficulty of edges during the masking step. Then, we adopt a self-paced scheduler to determine the order of masking edges, which encourages the graph encoder to learn from easy to difficult parts. Finally, the masked edges are gradually incorporated into the reconstruction pretext task, leading to high-quality node representations. Experiments on several real-world node classification and link prediction datasets demonstrate the superiority of our proposed method over state-of-the-art graph self-supervised learning baselines. This work is the first study of curriculum strategy for masked graph autoencoders, to the best of our knowledge.

Drug-drug interaction (DDI) prediction is a pivotal task in biomedical research. Emerging multimodal approaches that integrate graph neural networks (GNNs) and large language models (LLMs) have gained traction, as GNNs capture molecular structures while LLMs provide a rich biomedical context. However, real-world DDI data often exhibit distribution shifts across structural and textual dimensions, stemming from variations in molecular scaffolds, drug sizes, and assay conditions. Existing methods assume an independent and identically distributed (I.I.D.) setting, failing to handle such shifts primarily due to there key limitations: (i) the entanglement of core interaction motifs with incidental structural features; (ii) inflexible message-passing GNN architectures ill-suited for diverse drug pairs; and (iii) underutilized biomedical knowledge in LLMs for capturing pairwise interaction semantics. These limitations highlight the need for a disentangled, dynamic, and pairwise-aware modeling strategy to achieve out-of-distribution generalized DDI prediction. To solve this problem, we propose DyNamic Pairwise Architecture Search for Generalizable Drug-Drug Interaction LLM (DyNAS-DDI), a novel framework that dynamically adapts network architectures for each molecular pair and integrates biomedical knowledge from LLMs to improve generalization under distribution shifts. Specifically, we propose three modules: (i) Motif-driven disentangled molecule encoding, which disentangles molecular representations into distinct motif-based features while preserving key structural signals through a self-supervised graph encoder; (ii) Attentionbased pairwise neural architecture search, where multi-head attention enriches molecular features to guide a dynamic search mechanism that adaptively optimizes message passing for diverse interaction types; and (iii) retrieval-augmented molecular instruction tuning, where external biomedical knowledge is incorporated to improve interpretability and enable reasoning for unseen drug interactions. Extensive experiments on four datasets for DDI with out-of-distribution (OOD) splits demonstrate our method's superior generalization abilities under distribution shifts. Our code can be available at https://github.com/EkkoXiao/DyNAS-DDI.