LLM-enhanced Graph Contrastive Learning for Technology Flow Forecasting

Technology flow (TF) forecasting aims to predict whether a technology will be utilized by another, which is significant for future cross-integration technology innovation. Although existing methods have made substantial progress in intra-domain TF forecasting, inter-domain TF forecasting remains underexplored and faces several fundamental challenges. Specifically, semantic gaps between technologies from different domains hinder effective representation learning; historical inter-domain TFs are highly sparse, limiting the ability to learn reliable structural dependencies; and intra-domain and inter-domain TFs exhibit distinct flow patterns, which are difficult to model within a unified framework. To address these challenges, in this paper, we propose an LLM-enhanced Graph Contrastive Learning ( LGCL ) framework that supports both intra-domain and inter-domain TF forecasting. Specifically, we first leverage LLMs with bias-aware representation adjustment to generate semantically enriched and accurate technology representations to bridge gaps across multiple domains. Then, to mitigate the sparsity of inter-domain TF data, a technology co-occurrence view is incorporated to complement historical TF information. We further develop a non-negative contrastive learning module to integrate the TF and co-occurrence views, enabling effective representation alignment without relying on negative samples. In addition, we design decoupled loss functions to separately supervise intra-domain and inter-domain flows, mitigating the difficulty of jointly modeling their different flow patterns under a unified objective. Finally, experiments on real-world datasets validate the effectiveness of our approach and provide valuable insights into future technology flow trends. Our code is available at https://github.com/huijieliu2023/LGCL .