CG-GNN: A Novel Compiled Graphs-based Feature Extraction Method for Enterprise Social Networks

Tatsuya Konishi, Shuichiro Haruta, Mori Kurokawa, Kenta Tsukatsune, Yuto Mizutani, Tomoaki Saito, Hideki Asoh, Chihiro Ono

In this paper, we propose CG-GNN, a novel compiled graphs-based feature extraction method for Enterprise Social Networks (ESNs). For the provider of ESNs, extracting features from a given social graph is essential. However, since the amount of data available for a single enterprise is often limited, it is necessary to utilize data from other enterprises. We hypothesize that each enterprise has its own enterprise-specific features, while there is a general structure underlying in all enterprises. To reflect the hypothesis, our approach introduces “compiled graphs” to capture enterprise-specific features by mapping them through functions dedicated to that enterprise. The graphs are then handled by Graph Neural Networks (GNNs) that are commonly used across all enterprises to extract general structural information. Therefore, the obtained representations by CG-GNN are balanced in terms of enterprise-specific and enterprise-generic characteristics. Through experiments with private and publicly available datasets, we show that CG-GNN outperforms baselines by a large margin. In a practical scenario, we compute the ideal input of the proposed method for the purpose of ESNs revitalization. This experiment also demonstrates its feasibility and we believe the results are useful for many ESN providers.

4th Workshop on Intelligent Cross-Data Analysis and Retrieval

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