Predicting collaborative relationship among scholars by integrating scholars’ content-based and structure-based features

Abstract

Academic collaboration can break through the geographical limitations of scholars and promote academic output among scholars. Academic big data will provide an important data source for more comprehensive and accurate modeling scholars due to the coexistence environment of various academic entities. Based on academic big data, an end-to-end model HCSP was proposed for predicting collaborative relationships among scholars. HCSP models scholars from two aspects: content-based features and structure-based features, and calculate the similarity between scholars based on this to predict whether there will be academic collaboration between scholars. When learning the content-based features of scholars, HCSP utilizes LSTM and multi-head attention mechanism to extract the overall and recent research interests of scholars, to capture the diversity of scholars’ research interests. When learning the structure-based features of scholars, HCSP adopts a subgraph sampling strategy based on meta paths to model the structural features of scholar nodes in heterogeneous academic network. By integrating scholars’ content-based and structure-based features, HCSP calculates the similarity between scholars to determine whether there will be a collaborative relationship between them. The experimental results indicate that the HCSP model achieves better prediction performance compared to the baseline models. It can be seen that integrating scholars’ content-based and structure-based characteristics can indeed provide a richer and more effective modeling basis for predicting their academic collaborative relationships.