Tatsuya Konishi
ABSTRACT
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.
- Zhao Cai, Qian Huang, Hefu Liu, and Xiaoyan Wang. 2018. Improving the agility of employees through enterprise social media: The mediating role of psychological conditions. International Journal of Information Management 38, 1 (2018), 52–63.Google Scholar
Digital Library
- Jyoti Choudrie and Efpraxia D Zamani. 2016. Understanding individual user resistance and workarounds of enterprise social networks: the case of Service Ltd. Journal of Information Technology 31, 2 (2016), 130–151.Google ScholarCross Ref
- William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Proc. of NeurIPS. 1024–1034.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural computation 9, 8 (1997), 1735–1780.Google ScholarDigital Library
- Qiong Jia, Yang Lei, Yue Guo, and Xiaotong Li. 2022. Leveraging enterprise social network technology: understanding the roles of compatibility and intrinsic motivation. Journal of Enterprise Information Management 35, 6 (2022), 1764–1788.Google ScholarCross Ref
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proc. of ICLR.Google Scholar
- Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and VS Subrahmanian. 2018. Rev2: Fraudulent user prediction in rating platforms. In Proc. of WSDM. 333–341.Google ScholarDigital Library
- Srijan Kumar, Francesca Spezzano, VS Subrahmanian, and Christos Faloutsos. 2016. Edge weight prediction in weighted signed networks. In Proc. of ICDM. 221–230.Google ScholarCross Ref
- Quoc V. Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, and Andrew Y. Ng. 2012. Building high-level features using large scale unsupervised learning. In Proc. of ICML. 507–514.Google Scholar
- Paul M Leonardi, Marleen Huysman, and Charles Steinfield. 2013. Enterprise social media: Definition, history, and prospects for the study of social technologies in organizations. Journal of computer-mediated communication 19, 1 (2013), 1–19.Google ScholarCross Ref
- Aravindh Mahendran and Andrea Vedaldi. 2014. Understading Deep Image Representations by Inverting Them. In Proc. of CVPR. 5188–5196.Google Scholar
- Anam Nusrat, Yong He, Adeel Luqman, Abdul Waheed, and Amandeep Dhir. 2021. Enterprise social media and cyber-slacking: A Kahn’s model perspective. Information & Management 58, 1 (2021), 103405.Google ScholarCross Ref
- Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2017. Modeling Relational Data with Graph Convolutional Networks. In Proc. of ESWC. 593–607.Google Scholar
- Emma Spiro, J Nathan Matias, and Andrés Monroy-Hernández. 2016. Networks of gratitude: Structures of thanks and user expectations in workplace appreciation systems. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 10. 358–367.Google Scholar
- Veličković, Petar, Cucurull, Guillem, Casanova, Arantxa, Romero, Adriana, Lio, Pietro, Bengio, and Yoshua. 2018. Graph Attention Networks. In Proc. of ICLR.Google Scholar
Index Terms
- CG-GNN: A Novel Compiled Graphs-based Feature Extraction Method for Enterprise Social Networks
Recommendations
Feature Selection and Extraction for Graph Neural Networks
ACM SE '20: Proceedings of the 2020 ACM Southeast ConferenceGraph Neural Networks (GNNs) have been a latest hot research topic in data science, due to the fact that they use the ubiquitous data structure graphs as the underlying elements for constructing and training neural networks. In a GNN, each node has ...
Enterprise Social Link Recommendation
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge ManagementMany companies have started to use Enterprise Social Networks (ESNs), such as Yammer, to facilitate collaboration and communication amongst their employees in the business context. Social link recommendation, which finds and suggests whom one wants to ...
A Framework for Interfacing Unstructured Data Into Business Process From Enterprise Social Networks
The increased number of Enterprise Social Networks ESN business applications has had a major impact on organizations' business processes improvements by allowing the involvement of human interactions to these process. However, these applications ...
Comments