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Community-based service ecosystem evolution analysis

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Abstract

Services are flourishing dramatically, and continuously increasing interactions among them are resulting in a new phenomenon called “service ecosystems,” which has become a focus of academia and industry. Driven by technology innovation, changes in regulations, and changes in the competitive strategies of individual businesses, service ecosystems are constantly evolving. Service ecosystem evolution analysis is an emerging research problem of great significance. By analyzing service ecosystem evolution history, common evolution patterns can be identified, underlying driving forces can be discovered, and future evolution trends can be predicated so that service providers can adjust their competitive strategies in a timely manner to adapt to evolution trends. In this paper, a framework for identifying service ecosystem evolution patterns from the service community perspective is presented. First, following the approaches of community detection and community evolution analysis, time-series community evolution traces are identified from historical service ecosystem evolution, and a service community evolution prediction model is trained in accordance with such traces. Second, the prediction model is explained to show how different factors affect the evolution of service communities. Finally, an approach for assisting service providers in making business decisions is presented according to interpretable prediction results and prior domain knowledge. Experiments on a real-world dataset showed that this work can indeed provide business-level insights on service ecosystem evolution. Additionally, all the data and source code have been made fully open-source for service ecosystem researchers.

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Notes

  1. https://www.ibm.com/smarterplanet/us/en/.

  2. In the remainder of this paper, we consider this subgraph as a service ecosystem.

  3. https://github.com/icecity96/service_community_evlution_analysis.

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Acknowledgements

Research in this paper is partially supported by the National Key Research and Development Program of China (No 2021YFB3300700), the National Natural Science Foundation of China (61832004, 61832014).

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Authors and Affiliations

  1. Faculty of Computing, Harbin Institute of Technology, Harbin, China

    Mingyi Liu, Zhiying Tu, Hanchuan Xu, Xiaofei Xu & Zhongjie Wang

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  1. Mingyi Liu
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  2. Zhiying Tu
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  3. Hanchuan Xu
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  4. Xiaofei Xu
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  5. Zhongjie Wang
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Correspondence to Zhongjie Wang.

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Liu, M., Tu, Z., Xu, H. et al. Community-based service ecosystem evolution analysis. SOCA 16, 97–110 (2022). https://doi.org/10.1007/s11761-022-00333-9

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  • DOI: https://doi.org/10.1007/s11761-022-00333-9

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