Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

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

Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information

Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features make the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. Therefore, the correlations between vertex features can be better integrated into the model. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning

Analysis on Related Factors of Accident Tendency of Bus Drivers in Haikou City

Objective To understand the current situation of accident tendency of bus drivers in Haikou City, and to provide data reference for preventing bus traffic accidents in Haikou City. Methods: A total of 512 bus drivers with driving age ≥ 5 years were investigated by self-made questionnaire by random cluster sampling. The collected data were statistically analyzed by SPSS 26.0. Results: There were significant differences in accident proneness among bus drivers with different family pressure, relationship between different family members, different driving age, different driving time per day and different sleep time per day (P < 0.05). Conclusion: Drivers with high family pressure are easy to cause accidents many times, and drivers with general or disharmonious family members, lower driving age, less sleep time per day and longer driving time per day are easy to cause traffic accidents. Therefore, the relevant departments should take relevant measures according to these factors to reduce the incidence of bus drivers traffic accidents in Haikou City