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Abstract

This research employs an advanced Graph Attention Network (GAT) model to assess user influence in e-commerce social networks. As social networks become increasingly embedded in online shopping environments, it is essential for businesses to comprehend how user relationships, behaviors, and interactions shape purchasing behavior in order to refine marketing tactics. Conventional influence analysis techniques are inadequate for handling the intricate and varied nature of these networks, which include diverse user engagements and transaction records. This research proposes a modified GAT model tailored to e-commerce environments, which incorporates advanced attention mechanisms to better capture the significance of various user interactions, product categories, and demographic attributes. The study first constructs a graph-based representation of the e-commerce social network by integrating transactional, demographic, and social connection data. It then applies the enhanced GAT model to quantify user influence, emphasizing the importance of contextual interactions and varying user behaviors. Experimental results, validated using real-world e-commerce data, show that the modified GAT model outperforms traditional influence metrics in terms of accuracy, offering businesses actionable insights into identifying key influencers within their networks. Key contributions of the research include the development of a comprehensive graph-based framework for influence analysis, the introduction of domain-specific modifications to GAT, and the empirical validation of the proposed model’s effectiveness. These developments allow for more precise detection of key influencers, supporting e-commerce platforms in enhancing marketing efficiency, strengthening customer interaction, and boosting sales outcomes. By introducing a new method of influence analysis specifically designed for e-commerce social networks, this study addresses important gaps in existing research and provides a foundation for future investigations in the field.

Keywords

e-commerce social networks user influence graph attention networks (GAT)influence analysis

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References

Friedrich

Schlauderer

Overhage

. The impact of social commerce feature richness on website stickiness through cognitive and affective factors: An experimental study. Electronic Commerce Research and Applications 2019; 36(100861).

Gunawan Celine

Laili

, et al. The influence of social influence and peer influence on intention to purchase in e-commerce. Review of Management and Entrepreneurship 2023; 7(1): 61–84.

Handoyo

. Purchasing in the digital age: A meta-analytical perspective on trust, risk, security, and e-WOM in e-commerce. Heliyon 2024; 10(8).

Zahara AN Endang SR , et al. The influence of seller reputation and online customer reviews towards purchase decisions through consumer trust from C2C e-commerce platform users in Medan, North Sumatera, Indonesia. International Journal of Research and Review 2021; 8(2): 422–438.

Kaiyi

. The impact of social media on consumer behavior. Marketing 2023; 23.

Semenda

Sokolova

Korovina

, et al. Using social media analysis to improve E-commerce marketing strategies. International Review of Management and Marketing 2024; 14(4): 61–71.

Litterio

Nantes

Larrosa

, et al. Marketing and social networks: a criterion for detecting opinion leaders. European Journal of Management and Business Economics 2017; 26(3): 347–366.

Cárdenas

. Networking for innovation: an analysis of research on social networks, social capital, and innovation. International Review of Sociology 2021; 31(3): 392–409.

Anastasiei

Dospinescu

, et al. Word-of-mouth engagement in online social networks: Influence of network centrality and density. Electronics 2023; 12(13): 2857.

10.

Meng

Liu

Zhang

, et al. Temporal-aware and multifaceted social contexts modeling for social recommendation. Knowledge-Based Systems 2022; 248: 108923.

11.

Liu

Zheng

. The persuasive power of social media influencers in brand credibility and purchase intention. Humanities and Social Sciences Communications 2024; 11(1): 1–12.

12.

Ram R Rizoiu MA . Empirically measuring online social influence. EPJ Data Science 2024; 13(1): 53.

13.

Zhou

Cui

, et al. Graph neural networks: A review of methods and applications. AI open 2020; (1): 57–81.

14.

Pan

Chen

, et al.

A comprehensive survey on graph neural networks[J].

IEEE transactions on neural networks and learning systems 2020; 32(1): 4–24.

15.

Chen

Zaki

. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. Advances in neural information processing systems 2020; (33): 19314–19326.

16.

Khemani

Patil

Kotecha

, et al. A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions. Journal of Big Data 2024; 11(1): 18.

17.

Qian

Zhang

, et al. A graph-neural-network-based social network recommendation algorithm using high-order neighbor information. Sensors 2022; 22(19): 7122.

18.

Tsai

Chen

, et al. Graph neural networks for tabular data learning: A survey with taxonomy and directions. ACM Computing Surveys 2025; 58(1): 1–51.

19.

Munikoti

Agarwal

Das

. Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications. IEEE transactions on neural networks and learning systems 2023; 35(11): 15051–15071.

20.

Vrahatis

Lazaros

Kotsiantis

. Graph attention networks: a comprehensive review of methods and applications. Future Internet 2024; 16(9): 318.

21.

Sankar

Gou

, et al.

Dysat: Deep neural representation learning on dynamic graphs via self-attention

networks[C]//Proceedings of the 13th international conference on web search and data mining. 2020: 519–527.

22.

Anand

Maurya

. A survey on recommender systems using graph neural network. ACM Transactions on Information 2025; 43(1): 1–49.

23.

Wang

Zhang

Yuan

, et al. RLGAT: Retweet prediction in social networks using representation learning and GATs. Multimedia Tools and Applications 2024; 83(14): 40909–40938.

24.

Wang

, et al. TKGAT: Graph attention network for knowledge-enhanced tag-aware recommendation system. IEEE Transactions on Computational Social Systems 2022; (257): 109903.

25.

Najafabadi

Chen

Rezazadeh

, et al. From theory to practice: The evolution and comparative analysis of homogeneous vs. heterogeneous Graph Neural Networks in recommender systems. Neurocomputing 2025; (624): 129446.

26.

Natraj

Pethru Raj

. Graph Neural Networks: Architecting Intelligence Across Domains—From Molecular Design to Urban Systems[M]//Graph Neural Networks: Essentials and Use Cases. Cham: Springer Nature Switzerland 2025; 341–361.

Analysis of E-commerce user social network influence based on enhanced graph attention network algorithm

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