Locate Who You Are: Matching Geo-location to Text for Anchor Link Prediction

• URL: http://arxiv.org/abs/2104.09119v1
• Date: Mon, 19 Apr 2021 08:15:28 GMT
• Title: Locate Who You Are: Matching Geo-location to Text for Anchor Link Prediction
• Authors: Jiangli Shao, Yongqing Wang, Hao Gao, Huawei Shen, Xueqi Cheng
• Abstract summary: We propose a novel anchor link prediction framework for matching users across networks. Our approach outperforms existing methods and achieves state-of-the-art results.
• Score: 40.654494490237
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Nowadays, users are encouraged to activate across multiple online social networks simultaneously. Anchor link prediction, which aims to reveal the correspondence among different accounts of the same user across networks, has been regarded as a fundamental problem for user profiling, marketing, cybersecurity, and recommendation. Existing methods mainly address the prediction problem by utilizing profile, content, or structural features of users in symmetric ways. However, encouraged by online services, users would also post asymmetric information across networks, such as geo-locations and texts. It leads to an emerged challenge in aligning users with asymmetric information across networks. Instead of similarity evaluation applied in previous works, we formalize correlation between geo-locations and texts and propose a novel anchor link prediction framework for matching users across networks. Moreover, our model can alleviate the label scarcity problem by introducing external data. Experimental results on real-world datasets show that our approach outperforms existing methods and achieves state-of-the-art results.

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