Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

Existing studies for representation learning of homogeneous information networks cannot be applicable to academic heterogeneous information networks (HINs) with multi-type nodes and multi-relationships because of the lack of ability to issue heterogeneity. Meanwhile, due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network. Moreover, we combined federated learning with the representation learning of academic HINs and put forward a federal training mechanism based on dynamic weighted aggregation of parameters (FedDWA) to optimize the node embeddings of HINs. Through sufficient experiments, the efficiency, accuracy and feasibility of our proposed framework are demonstrated.