Heterogeneous Graph Structure Learning for Experts Selection in Academic Evaluation

Effective expert selection is an important guarantee for academic evaluation. Recently, graph structure learning (GSL) have been used to model the complex relationships between candidates and experts in both professional fields and avoidance strategies. Most existing GSL models aim to learn the structure of homogeneous graph. However, they cannot learn the structure of heterogeneous graph (HG) because they ignore the complex relation attributes in HG. To fill this gap, we propose a relation enhanced heterogeneous graph structure learning (RE-HGSL) model that refines HG structure and learns node and relation representations simultaneously for expert selection in academic evaluation. The main idea of RE-HGSL is refining the HG structure by reconstructing edges through the similarity of relation triplet <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt;$</tex-math></inline-formula>head node, relation, tail node<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula>. Specifically, we first set relation representation vector for each kind of relation to capture multiple relation attributes and learn node and relation representations to preserve multiple semantic information. Then, we construct multiple feature graphs where the edges are constructed according to the similarity of the relation triplet. Finally, the refined graph structure is generated by aggregating the original graph and the feature graphs, which maintains not only the part of original vital structure but also the refined edges.