#CrossOSN linking

User Linkage

• {Kong13} Xiangnan Kong, Jiawei Zhang and Philip S. Yu. Inferring Anchor Links across Heterogeneous Social Networks. Proc. of ACM CIKM, 2013. Mna method can effectively infer the anchor links w.r.t. one-to-one constraint
• {Liu14} S. Liu, S. Wang, and et al. HYDRA: Large-scale Social Identity Linkage via Heterogeneous Behavior Modeling. In Proc. of ACM SIGMOD, 2014.
• {Goga15} O. Goga, P. Loiseau, R. Sommer, R. Teixeira, K. Gummadi. On the Reliability of Profile Matching Across Large Online Social Networks. Proc. of ACM KDD, 2015.
• {Riederer16} Christopher Riederer, Yunsung Kim, Augustin Chaintreau, Nitish Korula, and Silvio Lattanzi. Linking Users Across Domains with Location Data: Theory and Validation. Proc. of WWW, 2016. link accounts of the same user across datasets using just location data
• {Cao16} Xuezhi Cao, Yong Yu. BASS: A Bootstrapping Approach for Aligning Heterogenous Social Networks. Proc. of ECML/PKDD, 2016.
• {Shu16} Kai Shu, Suhang Wang, Jiliang Tang, Reza Zafarani and Huan Liu. User Identity Linkage across Online Social Networks: A Review. To appear: ACM SIGKDD Explorations Newsletter.
• {Jain16} Paridhi Jain, Ponnurangam Kumaraguru, Anupam Joshi: Other times, other values: leveraging attribute history to link user profiles across online social networks. Social Netw. Analys. Mining, 2016, 6(1): 85:1-85:16.
• {Zhong17} Changtao Zhong, Hau-wen Chan, Dmytro Karamshu, Dongwon Lee, Nishanth Sastry. Wearing Many (Social) Hats: How Different are Your Different Social Network Personae? Proc. of AAAI ICWSM, 2017. different norms on different networks; profile classification; gender and age differences
• {Chen17} Wei Chen, Hongzhi Yin, Weiqing Wang, Lei Zhao, Wen Hua, and Xiaofang Zhou. Exploiting Spatio-Temporal User Behaviors for User Linkage. Proc. of ACM CIKM, 2017. exploit the spatio-temporal behaviors in a continuous way to achieve user linkage
• {Chen18} Wei Chen, Hongzhi Yin, Weiqing Wang, Lei Zhao, Xiaofang Zhou. Effective and Efficient User Account Linkage Across Location Based Social Networks. Proc. of ICDE, 2018. propose a general method to perform user account linkage with location data by considering both effectiveness and efficiency simultaneously
• {Feng19} Jie Feng, Mingyang Zhang, Huandong Wang, Zeyu Yang, Chao Zhang, Yong Li, and Depeng Jin. DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data. Proc. of WWW, 2019. DPLink, an end-to-end deep learning based framework, to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties
• {Jiao19} Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yangyong Zhu. Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention. Proc. of ACM CIKM, 2019. predict both the intra-network social links as well as the inter-network anchor links across multiple aligned social networks
• {Zhou20} Fan Zhou, Kunpeng Zhang, Shuying Xie, Xucheng Luo. Learning to Correlate Accounts Across Online Social Networks: An Embedding-Based Approach. INFORMS Journal on Computing, 2020, 32(3):714–729. correlate accounts with high accuracy by exploiting the semantic information among accounts through random walks
• {Liu20} Baoxi Liu, Peng Zhang, Tun Lu, Ning Gu. A reliable cross-site user generated content modeling method based on topic model, Knowledge-Based Systems, 2020, 209:106435. [PDF] we first conduct an empirical study to investigate the characteristics of users' content sharing practices in cross-site context, based on which we propose a more reliable cross-site UGC model named CrossSite-LDA (C-LDA)
• {Chen20} Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Tong Chen, Bogdan Gabrys, and Katarzyna Musial. Multi-level graph convolutional networks for cross-platform Anchor Link Prediction. Proc. of ACM KDD, 2020. propose a novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner
• {Li21} Yongjun Li, Wenli Ji, Xing Gao, Yao Deng, Wei Dong, Dongxu Li. Matching user accounts with spatio-temporal awareness across social networks. Information Sciences, 2021, 570:1-15.
• {Liang21} Zhehan Liang, Yu Rong, Chenxin Li, Yunlong Zhang, Yue Huang, Tingyang Xu, Xinghao Ding, and Junzhou Huang. Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding. Proc. of ACM CIKM, 2021.
• {Zhang24} Peng Zhang, Qi Zhou, Tun Lu, Hansu Gu, Ning Gu. DeLink: An Adversarial Framework for Defending against Cross-site User Identity Linkage. To appear: ACM Transactions on the Web. build an adversarial framework - DeLink based on the thoughts of adversarial text generation to help people improve their social media screen names to defend against cross-site UIL

Cross-Site User Behavior Analysis

• {Irani11} Danesh Irani, Steve Webb, Calton Pu, Kang Li. Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks. IEEE Internet Computing, 2011, 15(3): 13-19.
• {Chen12} T. Chen, M. A. Kaafar, and et al. Is More Always Merrier? A Deep Dive Into Online Social Footprints. In Proc. of ACM WOSN, 2012.
• {Goga13} O. Goga, G. Friedland, and et al. Exploiting Innocuous Activity for Correlating Users Across Sites. In Proc. of WWW, 2013. a user’s location profile as a histogram that records how often we observe each location in her posts
• {Yuan13} Nicholas Jing Yuan, Fuzheng Zhang, Defu Lian, Kai Zheng, Siyu Yu, Xing Xie. We Know How You Live: Exploring the Spectrum of Urban Lifestyles. Proc. of ACM COSN, 2013. a tree-structured hierarchy summarizing the living patterns
• {Ottoni14} Raphael Ottoni, Diego Las Casas, et al. Of Pins and Tweets: Investigating how users behave across image- and text-based social networks. Proc. of AAAI ICWSM, 2014. Pinterest-Twitter links
• {Wang14} P. Wang, W. He, and J. Zhao. A Tale of Three Social Networks: User Activity Comparisons across Facebook, Twitter, and Foursquare. IEEE Internet Computing, 18(2):10–15, 2014.
• {Farseev15} A. Farseev, N. Liqiang, M. Akbari, and T.-S. Chua. Harvesting multiple sources for user profile learning: a Big data study. Proc. of ACM ICMR, 2015.
• {Zhang15} Yutao Zhang, Jie Tang, Zhilin Yang, Jian Pei, and Philip S. Yu. COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency. Proc. of ACM KDD, 2015.
• {Farahbakhsh16} Reza Farahbakhsh, Ángel Cuevas, Noël Crespi: Characterization of cross-posting activity for professional users across Facebook, Twitter and Google+. Social Netw. Analys. Mining, 2016, 6(1): 33:1-33:14.
• {Cao16b} Xuezhi Cao, Yong Yu. ASNets : A Benchmark Dataset of Aligned Social Networks for Cross-Platform User Modeling. Proc. of ACM CIKM, 2016. Aligning the social networks is beneficial for users, service providers and also researchers; Cross-Platform User Modeling; Anonymity-Protecting Strategy; Multi-Network Analysis
• {Zhong17} Changtao Zhong, Hau-wen Chan, Dmytro Karamshu, Dongwon Lee, Nishanth Sastry. Wearing Many (Social) Hats: How Different are Your Different Social Network Personae? Proc. of AAAI ICWSM, 2017. different norms on different networks; profile classification; gender and age differences
• {Zhang17} Peng Zhang, Haiyi Zhu, Tun Lu, Hansu Gu, Wenjian Huang, and Ning Gu. Understanding Relationship Overlapping on Social Network Sites: A Case Study of Weibo and Douban. Proc. ACM Hum.-Comput. Interact., 2017, 1(CSCW), 120:1-120:18.
• {Gong18} Qingyuan Gong, Yang Chen, Jiyao Hu, Qiang Cao, Pan Hui, Xin Wang. Understanding Cross-Site Linking in Online Social Networks. ACM Transactions on the Web, 2018, 12(4):25:1-25:29.
• {Wang19} Weiqing Wang, Hongzhi Yin, Xingzhong Du, Wen Hua, Yongjun Li, and Quoc Viet Hung Nguyen. Online User Representation Learning Across Heterogeneous Social Networks. Proc. of ACM SIGIR, 2019. propose MV-URL, a multi- view user representation learning model to enhance user modeling by integrating the knowledge from various networks
• {Gao21} Yuqi Gao, Jitao Sang, Chengpeng Fu, Zhengjia Wang, Tongwei Ren, Changsheng Xu. Metadata Connector: Exploiting Hashtag and Tag for Cross-OSN Event Search. IEEE Transactions on Multimedia, 2021, 23:510-523. introduce a novel cross-OSN framework to help integrate these cross-OSN information regarding the same event and provide an immersive experience for information retrieval

Prediction

• {Meo13} Pasquale De Meo, Emilio Ferrara, Fabian Abel, Lora Aroyo, Geert-Jan Houben. Analyzing user behavior across social sharing environments. ACM Transactions on Intelligent Systems and Technology, 2013, 5(1):14:1-14:31.
• {Zhong14} Changtao Zhong, Mostafa Salehi, Sunil Shah, Marius Cobzarenco, Nishanth Sastry, and Meeyoung Cha. Social bootstrapping: how pinterest and last.fm social communities benefit by borrowing links from facebook. Proc. of WWW, 2014.
• {Deng14} Zhengyu Deng, Ming Yan, Jitao Sang, Changsheng Xu. Twitter is Faster: Personalized Time-aware Video Recommendation from Twitter to YouTube. ACM Transactions on Multimedia Computing, Communications, and Applications, 2014, 11(2): 31:1-31:23, 2014. a personalized time-aware video recommendation solution for multimedia sharing platforms (e.g. YouTube) based on cross-platform collaboration from the social textual stream-based platforms (e.g. Twitter)
• {Venkatadri16} G. Venkatadri, O. Goga, C, Zhong, B.Viswanath, K. Gummadi, N. Sastry. Strengthening Weak Identities through Inter-Domain Trust Transfer. Proc. of WWW, 2016.
• {Sang18} Jitao Sang, Ming Yan, Changsheng Xu. Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation. IEEE Transactions on Multimedia, 2018, 20(12):3439-3451. propose a dynamic association mining framework to connect between different OSNs; learning of a transfer matrix W between different OSNs and continuously updating it with time.
• {Farseev17} A. Farseev and T.-S. Chua. TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning. Proc. of AAAI, 2017.
• {Zhu20} Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, and Qing He. Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection. Proceedings of The Web Conference, 2020.
• {Farseev17} A. Farseev, and T.-S. Chua. Tweet can be Fit: Integrating Data from Wearable Sensors and Multiple Social Networks for Wellness Profile Learning ACM Transactions on Information Systems (TOIS), 2017.
• {Farseev17} A. Farseev, I. Samborskii, A. Filchenkov, and T.-S. Chua. Cross-Domain Recommendation via Clustering on Multi-Layer Graphs. Proc. of ACM SIGIR, 2017.
• {Wang17} Xiang Wang, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. Item Silk Road: Recommending Items from Information Domains to Social Users. Proc. of ACM SIGIR, 2017.
• {Li23} Xinhang Li, Zhaopeng Qiu, Jiacheng Jiang, Yong Zhang, Chunxiao Xing, and Xian Wu. Conditional Cross-Platform User Engagement Prediction. To appear: ACM Trans. Inf. Syst. a new task: estimating the user engagement score of a media on one platform given its popularity on other platform