Profile Matching in Heterogeneous Academic Social Networks using Knowledge Graphs

Document Type : Semantic Technology-Kahani


1 Department of Engineering, Ferdowsi University, Mashhad, Iran

2 Department of Computer Engineering, Ferdowsi University of Mashhad

3 Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad

4 Department of Computer Science, University of Alberta, Edmonton, Canada


With the increasing popularity of academic social networks, many users join more than one network to benefit from their unique features. However, matching the profiles of a user, despite being crucial for data verification and update synchronization, is challenging due to the differences in profile structures across different networks. In this paper, we propose an academic profile-matching approach that utilizes an Academic Knowledge Graph (AKG) to overcome the diversity problem in profile structures. Our approach includes three components: (1) candidate profile generation, which retrieves related profiles from the target network based on name similarity to the source profile; (2) profile enrichment, which uses AKG to discover relations between the attributes of the source and target profiles; and (3) profile matching, which selects one candidate as a matched profile. Through experiments on real-world datasets, we demonstrate that the proposed approach is effective in matching academic profiles across different networks, outperforming state-of-the-art baselines.


Main Subjects

[1]    X. Kong, Y. Shi, S. Yu, J. Liu, and F. Xia, “Academic social networks: Modeling, analysis, mining and applications,” Journal of Network and Computer Applications, vol. 132. Academic Press, pp. 86–103, Apr. 15, 2019. doi: 10.1016/j.jnca.2019.01.029.
[2]    F. Xia, W. Wang, T. M. Bekele, and H. Liu, “Big Scholarly Data: A Survey,” IEEE Trans Big Data, vol. 3, no. 1, pp. 18–35, Jan. 2017, doi: 10.1109/tbdata.2016.2641460.
[3]    S. Liu, S. Wang, F. Zhu, J. Zhang, and R. Krishnan, “HYDRA: Large-scale social identity linkage via heterogeneous behavior modeling,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery, 2014, pp. 51–62. doi: 10.1145/2588555.2588559.
[4]    L. Wang, K. Hu, Y. Zhang, and S. Cao, “Factor Graph Model Based User Profile Matching across Social Networks,” IEEE Access, vol. 7, pp. 152429–152442, 2019, doi: 10.1109/ACCESS.2019.2948073.
[5]    K. Shu, S. Wang, J. Tang, R. Zafarani, and H. Liu, “User Identity Linkage across Online Social Networks: A Review.” [Online]. Available:
[6]    Y. Huang, P. Zhao, Q. Zhang, L. Xing, H. Wu, and H. Ma, “A Semantic-Enhancement-Based Social Network User-Alignment Algorithm,” Entropy, vol. 25, no. 1, Jan. 2023, doi: 10.3390/e25010172.
[7]    V. Sharma and C. Dyreson, “Linksocial: Linking user profiles across multiple social media platforms,” in Proceedings - 9th IEEE International Conference on Big Knowledge, ICBK 2018, Institute of Electrical and Electronics Engineers Inc., Dec. 2018, pp. 260–267. doi: 10.1109/ICBK.2018.00042.
[8]    Y. Li, W. Ji, X. Gao, Y. Deng, W. Dong, and D. Li, “Matching user accounts with spatio-temporal awareness across social networks,” Inf Sci (N Y), vol. 570, pp. 1–15, Sep. 2021, doi: 10.1016/j.ins.2021.04.030.
[9]    M. Färber, “The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data”, doi: 10.5281/zenodo.2159723.
[10] K. Deng, L. Xing, L. Zheng, H. Wu, P. Xie, and F. Gao, “A User Identification Algorithm Based on User Behavior Analysis in Social Networks,” IEEE Access, vol. 7, pp. 47114–47123, 2019, doi: 10.1109/ACCESS.2019.2909089.
[11] A. T. Hadgu, J. Kumar, and R. Gundam, “Learn2Link: Linking the Social and Academic Profiles of Researchers,” 2020. [Online]. Available:
[12] Y. Qu, L. Xing, H. Ma, H. Wu, K. Zhang, and K. Deng, “Exploiting User Friendship Networks for User Identification across Social Networks,” Decis Support Syst, vol. 14, no. 1, Jan. 2022, doi: 10.3390/sym14010110.
[13] R. Wang, H. Zhu, L. Wang, Z. Chen, M. Gao, and Y. Xin, “User identity linkage across social networks by heterogeneous graph attention network modeling,” Applied Sciences (Switzerland), vol. 10, no. 16, Aug. 2020, doi: 10.3390/app10165478.
[14] X. Chen, X. Song, G. Peng, S. Feng, and L. Nie, “Adversarial-Enhanced Hybrid Graph Network for User Identity Linkage,” in SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, Inc, Jul. 2021, pp. 1084–1093. doi: 10.1145/3404835.3462946.
[15] Stefano. Leonardi, ACM Digital Library., Association for Computing Machinery. Special Interest Group on Information Retrieval., H. and Web. Association for Computing Machinery. Special Interest Group on Hypertext, Association for Computing Machinery. Special Interest Group on Knowledge Discovery & Data Mining., and Association for Computing Machinery. Special Interest Group on Management of Data., “Hon, What’s in a name?: An unsupervised approach to link users across communities,” p. 798, 2013.
[16] D. Perito, C. Castelluccia, M. A. Kaafar, and P. Manils, “How Unique and Traceable Are Usernames?,” Proceedings of 11th International Conference on Privacy Enhancing Technologies, 2011, pp. 1–17..
[17] Y. Li, Y. Peng, W. Ji, Z. Zhang, and Q. Xu, “User Identification Based on Display Names Across Online Social Networks,” IEEE Access, vol. 5, pp. 17342–17353, Aug. 2017, doi: 10.1109/ACCESS.2017.2744646.
[18] Y. Li, Z. Zhang, Y. Peng, H. Yin, and Q. Xu, “Matching user accounts based on user generated content across social networks,” Future Generation Computer Systems, vol. 83, pp. 104–115, Jun. 2018, doi: 10.1016/j.future.2018.01.041.
[19] C. Yong. Chan, ACM Digital Library, H. and Web. Association for Computing Machinery. Special Interest Group on Hypertext, and Association for Computing Machinery. Special Interest Group on Information Retrieval, I Seek You: Searching and Matching Individuals In Social Networks. ACM, 2009.
[20] X. Kong, J. Zhang, and P. S. Yu, “Inferring anchor links across multiple heterogeneous social networks,” in International Conference on Information and Knowledge Management, Proceedings, 2013, pp. 179–188. doi: 10.1145/2505515.2505531.
[21] C. Shi and R. Duan, “Multiresolution Mutual Information Method for Social Network Entity Resolution,” in Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Institute of Electrical and Electronics Engineers Inc., Jan. 2016, pp. 240–247. doi: 10.1109/ICDMW.2015.94.
[22] C. Riederer, Y. Kim, A. Chaintreau, N. Korula, and S. Lattanzi, “Linking users across domains with location data: Theory and validation,” in 25th International World Wide Web Conference, WWW 2016, International World Wide Web Conferences Steering Committee, 2016, pp. 707–719. doi: 10.1145/2872427.2883002.
[23] L. W. S. X. G. L. and D. Z. Xiaohui Han, “Linking social network accounts by modeling user spatiotemporal habits,” in 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), 2017.
[24] Z. Yin, Y. Yang, and Y. Fang, “Link User Identities Across Social Networks Based on Contact Graph and User Social Behavior,” IEEE Access, vol. 10, pp. 42432–42440, 2022,
doi: 10.1109/ACCESS.2022.3165568.
[25] W. Chen, W. Wang, H. Yin, L. Zhao, and X. Zhou, “HFUL: A Hybrid Framework for User Account Linkage across Location-Aware Social Networks,” Jan. 2022, [Online]. Available:
[26] H. Gao, Y. Wang, J. Shao, H. Shen, and X. Cheng, “User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function,” Entropy, vol. 24, no. 11, Nov. 2022, doi: 10.3390/e24111603.
[27] Y. Qu, H. Ma, H. Wu, K. Zhang, and K. Deng, “A Multiple Salient Features-Based User Identification across Social Media,” Entropy, vol. 24, no. 4, Apr. 2022, doi: 10.3390/e24040495.
[28] T. Man, H. Shen, S. Liu, X. Jin, and X. Cheng, “Predict Anchor Links across Social Networks via an Embedding Approach.”
[29] Q. Miao, L. Wang, D. Duan, X. Guo, and X. Li, “Embedding Based Cross-network User Identity Association Technology,” in PervasiveHealth: Pervasive Computing Technologies for Healthcare, ICST, 2019, pp. 138–143.
doi: 10.1145/3316551.3316571.
[30] A. Cheng, C. Y. Liu, C. Zhou, J. Tan, and L. Guo, “User Alignment via Structural Interaction and Propagation,” in Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., Oct. 2018.
doi: 10.1109/IJCNN.2018.8489228.
[31] Y. Li, Z. Su, J. Yang, and C. Gao, “Exploiting similarities of user friendship networks across social networks for user identification,” Inf Sci (N Y), vol. 506, pp. 78–98, Jan. 2020, doi: 10.1016/j.ins.2019.08.022.
[32] X. Zou, “A Survey on Application of Knowledge Graph,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Apr. 2020. doi: 10.1088/1742-6596/1487/1/012016.
[33] N. Verdugo, E. Guzmán, and C. Urdiales, “Integrating researchers’ scientific production information through Ogmios,” Knowl Inf Syst, vol. 62, no. 11, pp. 4199–4222, Nov. 2020, doi: 10.1007/s10115-020-01479-8.
[34] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Oct. 2018, [Online]. Available: