Dynamic link prediction in construction innovation networks: An integrated framework of topological and content attributes

Abstract

Accurately predicting the evolution of collaborative relationships in construction innovation organizations is crucial for optimizing subsequent innovation decisions and developing inter-organizational collaboration strategies. Regrettably, prior research has accorded limited attention to link prediction within construction innovation collaborative networks. Consequently, this study introduces a novel link prediction approach that forecasts inter-node connectivity relationships by integrating topological structure characteristics and node content attributes of these networks. The proposed metrics leverage primary and secondary measures based on temporal events to assess the influence of nodes and their neighbors on the prediction outcomes. Through a comprehensive set of experiments, the study systematically assessed the performance of the proposed metrics across diverse link prediction scenarios. The results indicate that the proposed metric consistently outperforms several well-established baseline methods, yielding highly encouraging outcomes. This research not only enriches the theoretical underpinnings of network link prediction and construction innovations but also provides valuable insights into the evolution of collaborative relationships and the identification of potential partners within innovation organizations.