Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

TL;DR

This paper introduces FedAHE, a federated learning-based method for embedding heterogeneous academic networks, preserving complex information while addressing data isolation issues.

Contribution

It proposes a novel federated learning framework for HIN embedding that combines node and meta-path attention mechanisms, enabling effective representation learning across data islands.

Findings

Demonstrates high accuracy and efficiency in experiments

Preserves rich topological and semantic information

Addresses data isolation in academic networks

Abstract

Academic networks in the real world can usually be portrayed as heterogeneous information networks (HINs) with multi-type, universally connected nodes and multi-relationships. Some existing studies for the representation learning of homogeneous information networks cannot be applicable to heterogeneous information networks because of the lack of ability to issue heterogeneity. At the same time, data has become a factor of production, playing an increasingly important role. Due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, aiming at the data information of scientific research teams closely related to science and technology, 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 HINs composed of scientific research teams 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.