{Scellato11b} Salvatore Scellato, Anastasios Noulas, Cecilia Mascolo. Exploiting Place Features in Link Prediction on Location-based Social Networks. Proc. of KDD, 2011.
{Noulas12} Anastasios Noulas, Salvatore Scellato, Neal Lathia and Cecilia Mascolo. Mining User Mobility Features for Next Place Prediction in Location-based Services. Proc. of IEEE ICDM, 2012.
{Dong12} Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nitesh V. Chawla, Jinghai Rao, Huanhuan Cao. Link Prediction and Recommendation across Heterogeneous Social Networks. Proc. of IEEE ICDM, 2012. ranking factor graph model (RFG) for predicting links | social balance
{Dong14} Yuxiao Dong, Yang Yang et al. Inferring User Demographics and Social Strategies in Mobile Social Networks. Proc. of ACM KDD, 2014. automatically infer users' demographics based on their daily mobile communication patterns
{Lian14} Defu Lian, Yin Zhu, Xing Xie, Enhong Chen. Analyzing Location Predictability on Location-Based Social Networks. Proc. of PAKDD, 2014.
{Zhong15} Changtao Zhong, Dmytro Karamshuk, Nishanth Sastry. Predicting Pinterest: Automating a Distributed Human Computation. Proc. of WWW, 2015. predict whether a user will repin an image onto her own pinboard, and also which pinboard she might choose
{Vasconcelos15} Marisa Vasconcelos, Jussara M. Almeida, Marcos André Gonçalves. Predicting the popularity of micro-reviews: A Foursquare case study. Information Sciences, 2015, 325:355-374.
{Jia16} Yongpo Jia, Xuemeng Song, Jingbo Zhou, Li Liu, Liqiang Nie, David S. Rosenblum. Fusing Social Networks with Deep Learning for Volunteerism Tendency Prediction. Proc. of AAAI, 2016. a novel model that fuses social networks using deep learning with source confidence and consistency regularization | predict individuals’ tendency to volunteerism
{Lo16} Caroline Lo, Dan Frankowski, Jure Leskovec. Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest. Proc. of KDD, 2016. analyze the purchasing behavior of nearly three million Pinterest users to determine short-term and long-term signals in user behavior that indicate higher purchase intent; identifies a set of general principles and signals that can be used to model user purchasing intent across many content discovery applications.
{Lo17} Caroline Lo, Justin Cheng, Jure Leskovec. Understanding Online Collection Growth Over Time: A Case Study of Pinterest. Proc. of WWW Companion, 2017.
{Wei17} Honghao Wei, Fuzheng Zhang, Nicholas Jing Yuan, Chuan Cao, Hao Fu, Xing Xie, Yong Rui, Wei-Ying Ma. Beyond the Words: Predicting User Personality from Heterogeneous Information. Proc. of ACM WSDM, 2017. predict users’ personality traits by integrating heterogeneous information including self-language usage, avatar, emoticon, and responsive patterns
{Cheng17} Justin Cheng, Caroline Lo, Jure Leskovec. Predicting Intent Using Activity Logs: How Goal Specificity and Temporal Range Affect User Behavior. Proc. of WWW Companion, 2017. goal specificity is bimodal – users tend to be either strongly goal-specific or goal- nonspecific | building a model that can predict a user’s intent for using Pinterest after observing their activity for only two minutes
{Suhara17} Yoshihiko Suhara, Yinzhan Xu, Alex Sandy Pentland: DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks. WWW 2017: 715-724 RNN-based mood forecasting
{Zafar17} Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez and Krishna P. Gummadi. Fairness Beyond Disparate Treatment and Disparate Impact: Learning Classification Without Disparate Mistreatment. Proc. of WWW, 2017. effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy
{Hu18} Wenjian Hu, Krishna Kumar Singh, Fanyi Xiao, Jinyoung Han, Chen-Nee Chuah, Yong Jae Lee. Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks. Proc. of ACM WSDM, 2018.
{Kwon19} Young D. Kwon, Dimitris Chatzopoulos, Ehsan ul Haq, Raymond Chi-Wing Wong, and Pan Hui. GeoLifecycle: User Engagement of Geographical Exploration and Churn Prediction in LBSNs. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2019, 3(3): Article 92. long-term producers v.s. ordinary producers; users exhibit exploring behaviors until the end of their life in LBSNs; churning users and staying users show different patterns
{Liu19} Yozen Liu, Xiaolin Shi, Lucas Pierce, Xiang Ren. Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat. Proc. of ACM KDD, 2019. propose a GCN-LSTM model for learning from temporal action graph and develop a multi-channel end-to-end forecasting framework for integrating with other useful signalsPDF
{Ye19} Yuyang Ye, Hengshu Zhu, Tong Xu, Fuzhen Zhuang, Runlong Yu, Hui Xiong. Identifying High Potential Talent: A Neural Network based Dynamic Social Profiling Approach. Proc. of IEEE ICDM, 2019.
{Haldar19} Nur Al Hasan Haldar, Jianxin Li, Mark Reynolds, Timos Sellis, Jeffrey Xu Yu. Location prediction in large-scale social networks: an in-depth benchmarking study. VLDB Journal, 2019, 28(5): 623-648.
{Cao20} Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, and Xueqi Cheng. Popularity Prediction on Social Platforms with Coupled Graph Neural Networks. Proc. of ACM WSDM, 2020. propose a novel method, namely CoupledGNN, which uses two cou-pled graph neural networks to capture the interplay between nodeactivation states and the spread of influence
{Gao20} Xin Gao, Jar-Der Luo, Kunhao Yang et al. Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks. Journal of Social Computing, 2020, 1(1): 40-52.
{Tang20} Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra, and Suhang Wang. Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps. Proc. of ACM KDD, 2020. design an end-to-end neural framework, FATE, which incorporates three key factors that we identify to influence user engagement, namely friendships, user actions, and temporal dynamics to achieve explainable engagement predictions
Cross-Site Prediction
{Bai17} Bai, T., Dou, HJ., Zhao, W.X. et al. An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data. Journal of Computer Science and Technology, 2017, 32:828–842. [PDF] focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform | Weibo=>JD