A5101402154- Advanced Graph Neural Networks
- AI and HR Technologies
- Complex Network Analysis Techniques
- Recommender Systems and Techniques
- Customer churn and segmentation
- Employer Branding and e-HRM
Chongqing University
2019-2023
The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time. Most past studies on have focused analyzing impact factors or using simple network centrality measures. In this paper, we study the problem from a completely new perspective by modeling users' historical job records as dynamic bipartite graph. Specifically, propose graph embedding method with temporal information called (DBGE) to learn vector representation employees...
In human resource management, employee turnover prediction is very important for company operation since the leave of key employees can bring great loss to companies. However, most existing researches focused on employee-centered prediction, while ignored historical events behaviors as well longitudinal data each work. Therefore, in this paper we propose a algorithm named CoxRF from an event-centered perspective, which combines statistical results survival analysis with ensemble learning and...
Many real-world data can be represented as heterogeneous graphs with different types of nodes and connections. Heterogeneous graph neural network model aims to embed or subgraphs into low-dimensional vector space for various downstream tasks such node classification, link prediction, etc. Although several models were proposed recently, they either only aggregate information from the same type neighbors, just indiscriminately treat homogeneous neighbors in way. Based on these observations, we...