Jing Zhang
Hong Chen
ABSTRACT
Aligning users across multiple heterogeneous social networks is a fundamental issue in many data mining applications. Methods that incorporate user attributes and network structure have received much attention. However, most of them suffer from error propagation or the noise from diverse neighbors in the network. To effectively model the influence from neighbors, we propose a graph neural network to directly represent the ego networks of two users to be aligned into an embedding, based on which we predict the alignment label. Three major mechanisms in the model are designed to unitedly represent different attributes, distinguish different neighbors and capture the structure information of the ego networks respectively.
Systematically, we evaluate the proposed model on a number of academia and social networking datasets with collected alignment labels. Experimental results show that the proposed model achieves significantly better performance than the state-of-the-art comparison methods (+3.12-30.57% in terms of F1 score).
- James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In NIPS'16. 1993--2001. Google Scholar
Digital Library
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR'15.Google Scholar
- David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In NIPS'15. 2224--2232. Google ScholarDigital Library
- Marco Gori, Gabriele Monfardini, and Franco Scarselli. 2005. A new model for learning in graph domains. In IJCNN'05. 729--734.Google ScholarCross Ref
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD'15. 855--864. Google ScholarDigital Library
- Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR'17.Google Scholar
- Xiangnan Kong, Jiawei Zhang, and Philip S. Yu. 2013. Inferring anchor links across multiple heterogeneous social networks. In CIKM'13. 179--188. Google ScholarDigital Library
- Nitish Korula and Silvio Lattanzi. 2014. An efficient reconciliation algorithm for social networks. Proceedings of the VLDB Endowment 7, 5 (2014), 377--388. Google ScholarDigital Library
- Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, and Zoubin Ghahramani. 2013. Sigma: Simple greedy matching for aligning large knowledge bases. In SIGKDD'13. 572--580. Google ScholarDigital Library
- Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2016. Gated graph sequence neural networks. In ICLR'16.Google Scholar
- Jing Liu, Fan Zhang, Xinying Song, Young-In Song, Chin-Yew Lin, and HsiaoWuen Hon. 2013. What's in a name? An unsupervised approach to link users across communities. In WSDM'13. 495--504. Google ScholarDigital Library
- Li Liu, William K. Cheung, Xin Li, and Lejian Liao. 2016. Aligning Users across Social Networks Using Network Embedding. In IJCAI'16. 1774--1780. Google ScholarDigital Library
- Siyuan Liu, Shuhui Wang, Feida Zhu, Jinbo Zhang, and Ramayya Krishnan. 2014. Hydra: Large-scale social identity linkage via heterogeneous behavior modeling. In SIGMOD'14. 51--62. Google ScholarDigital Library
- Xuezhe Ma and Eduard Hovy. 2016. End-to-end Sequence Labeling via Bidirectional LSTM-CNNs-CRF. In ACL'16. 1064--1074.Google Scholar
- Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin, and Xueqi Cheng. 2016. Predict Anchor Links across Social Networks via an Embedding Approach. In IJCAI'16. 1823--1829. Google ScholarDigital Library
- Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In International conference on machine learning. 2014--2023. Google ScholarDigital Library
- Daniele Perito, Claude Castelluccia, Mohamed Ali Kaafar, and Pere Manils. 2011. How Unique and Traceable Are Usernames? In ICPET'11. 1--17. Google ScholarDigital Library
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In SIGKDD'14. 701--710. Google ScholarDigital Library
- Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009. The graph neural network model. TNN 20, 1 (2009), 61--80. Google ScholarDigital Library
- Kai Shu, Suhang Wang, Jiliang Tang, Reza Zafarani, and Huan Liu. 2017. User Identity Linkage across Online Social Networks: A Review. ACM SIGKDD Explorations Newsletter 18, 2 (2017), 5--17. Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In WWW'15. 1067--1077. Google ScholarDigital Library
- Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In SIGKDD'08. 990--998. Google ScholarDigital Library
- Petar Veličkovič, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR'18.Google Scholar
- Reza Zafarani and Huan Liu. 2009. Connecting Corresponding Identities across Communities. ICWSM'09 9 (2009), 354--357.Google Scholar
- Reza Zafarani and Huan Liu. 2013. Connecting users across social media sites: a behavioral-modeling approach. In SIGKDD'13. 41--49. Google ScholarDigital Library
- Reza Zafarani, Lei Tang, and Huan Liu. 2015. User identification across social media. TKDD 10, 2 (2015), 16. Google ScholarDigital Library
- Si Zhang and Hanghang Tong. 2016. FINAL: Fast Attributed Network Alignment. In SIGKDD'16. 1345--1354. Google ScholarDigital Library
- Yutao Zhang, Jie Tang, Zhilin Yang, Jian Pei, and Philip S. Yu. 2015. COSNET: Connecting heterogeneous social networks with local and global consistency. In SIGKDD'15. 1485--1494. Google ScholarDigital Library
- Zexuan Zhong, Yong Cao, Mu Guo, and Zaiqing Nie. 2018. CoLink: An Unsupervised Framework for User Identity Linkage. (2018).Google Scholar
- Xiaoping Zhou, Xun Liang, Haiyan Zhang, and Yuefeng Ma. 2016. Crossplatform identification of anonymous identical users in multiple social media networks. TKDE 28, 2 (2016), 411--424. Google ScholarDigital Library
Index Terms
- MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks
Recommendations
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementIntegrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by growing privacy ...
Investigating Homophily in Online Social Networks
WI-IAT '10: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01Similarity breeds connections, the principle of homophily, has been well studied in existing sociology literature. %Several studies have observed this phenomena by conducting surveys on human subjects. These studies have concluded that new ties are ...
MigrateSDN: efficient approach to integrate OpenFlow networks with STP-enabled networks
Software defined networking (SDN) is a paradigm-shifting technology in networking. However, in current network infrastructures, removing existing networks to build pure SDN networks or replacing all operating network devices with SDN-enabled devices is ...
Comments