III:Small:Discovering and Characterizing Implicit Links in Graph Data

Description

Social media greatly enable people to participate in online activities. A typical social networking site allows users to specify only positive links to other users such as friendships. Little attention is paid to ``negative links" which are implicit links among social users that indicate distrust, dislike, or antagonism. This project studies fundamental data analytics issues of understanding and identifying negative social network links. This project explores new research endeavors to mine actionable and insightful patterns in computer science and to enable the large-scale study of social media user behaviors in computational social science. The research insights gained through this project should benefit the design of new recommender systems and lead to better design of supervised and unsupervised learning algorithms on networks with both positive and negative links. The study of negative social network links can have impact on industrial IT applications by improving services and user experience. The proposed research will involve graduate and undergraduate students in pursuing their theses or projects. Research topics and findings will be integrated in undergraduate and graduate education. The research discovery made through this project will be shared at this project site.

Networks in social media are large-scale, incomplete, and noisy. As negative links between users are often invisible on social networking sites, discovering them entails novel challenges. The research team proposes to evaluate the value of negative links for better social network understanding and link discovery. They propose a group of data analytics techniques, including positive and unlabeled learning, learning with cross-media data, to efficiently predict negative links. The team also proposes to apply the research insights for the better design of recommendation systems, social user classification, and social user clustering. The research team plans to share benchmark data with the research community to promote the research on negative link discovery on social networks.

Publications

• Jundong Li, Jiliang Tang and Huan Liu. ``Recent Advances in Feature Selection: A Data Perspective", In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.

• Ling Jian, Jundong Li and Huan Liu. ``Exploiting Multi-Label Information for Noise Resilient Feature Selection", ACM Transactions on Intelligent Systems and Technology (TIST), 2018.
• Ling Jian, Jundong Li, Huan Liu. ``Toward Online Node Classification on Streaming Networks", Data Mining and Knowledge Discovery (DMKD), 32(1): 231-257, 2018.
• Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang and Huan Liu. ``Feature Selection: A Data Perspective", ACM Computing Surveyes (CSUR) 50(6): 94:1-94:45, 2017.
• Jundong Li and Huan Liu. ``Challenges of Feature Selection for Big Data Analytics", Special Issue on Big Data, IEEE Intelligent Systems, 32 (2), 9-15, 2017

• Jundong Li, Jiliang Tang, Yilin Wang, Yali Wan, Yi Chang and Huan Liu ``Understanding and Predicting Delay in Reciprocal Relations", In Proceedings of the 2018 Web Conference (WWW), 2018.
• Jundong Li, Chen Chen, Hanghang Tong, and Huan Liu. ``Multi-Layered Network Embedding", In Proceedings of the 18th SIAM International Conference on Data Mining (SDM), 2018.
• Jundong Li, Kewei Cheng, Liang Wu, and Huan Liu. ``Streaming Link Prediction on Dynamic Attributed Networks", In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), 2018.
• Xuying Meng, Suhang Wang, Huan Liu, and Yujun Zhang. ``Exploiting Emotion on Reviews for Recommender Systems", In Proceedings of the 32rd AAAI International Conference on Artificial Intelligence (AAAI), 2018.
• Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, and Yujun Zhang. ``Personalized Privacy-Preserving Social Recommendation", In Proceedings of the 32rd AAAI International Conference on Artificial Intelligence (AAAI), 2018.
• Jundong Li, Liang Wu, Harsh Dani, and Huan Liu. ``Unsupervised Personalized Feature Selection", In Proceedings of the 32rd AAAI International Conference on Artificial Intelligence (AAAI), 2018.
• Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. ``Attributed Network Embedding for Learning in a Dynamic Environment", In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM), 2017.
• Suhang Wang, Charu Aggarwal, Jiliang Tang, and Huan Liu. ``Attributed Signed Network Embedding", In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM), 2017.
• Harsh Dani, Jundong Li, and Huan Liu. ``Sentiment Informed Cyberbullying Detection in Social Media", In Proceedings of the 2017 European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2017.
• Jundong Li, Harsh Dani, Xia Hu, and Huan Liu. ``Radar: Residual Analysis for Anomaly Detection in Attributed Networks", In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.
• Jundong Li, Jiliang Tang, and Huan Liu. ``Reconstruction-based Unsupervised Feature Selection: An Embedded Approach", In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.
• Kewei Cheng, Jundong Li, and Huan Liu. ``Unsupervised Feature Selection in Signed Social Networks", In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.
• Suhang Wang, Charu Aggarwal, Huan Liu. ``Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods", In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.
• Suhang Wang, Charu Aggarwal, and Huan Liu. ``Using a Random Forest to Inspire a Neural Network and Improving on It", In Proceedings of the 17th SIAM International Conference on Data Mining (SDM), 2017.
• Suhang Wang, Yilin Wang, Jiliang Tang, Charu Aggarwal, Suhas Ranganath, and Huan Liu. ``Exploiting Hierarchical Structures for Unsupervised Feature Selection", In Proceedings of the 17th SIAM International Conference on Data Mining (SDM), 2017.
• Suhang Wang, Jiliang Tang, Charu Aggarwal, Yi Chang, and Huan Liu. ``Signed Network Embedding in Social Media", In Proceedings of the 17th SIAM International Conference on Data Mining (SDM), 2017.
• Jundong Li, Liang Wu, Osmar R. Zaïane and Huan Liu. ``Toward Personalized Relational Learning", In Proceedings of the 17th SIAM International Conference on Data Mining (SDM), 2017.
• Suhang Wang, Yilin Wang, Jiliang Tang, Kai Shu, Suhas Ranganath, and Huan Liu. ``Exploiting Visual Contents for Point-of-Interest Recommendation", The Proceedings of the 26th International World Wide Web Conference (WWW), 2017.
• Kewei Cheng, Jundong Li, Jiliang Tang, and Huan Liu. ``Unsupervised Sentiment Analysis with Signed Social Networks", In Proceedings of the 31st AAAI conference on Artificial Intelligence (AAAI), 2017.
• Jundong Li, Xia Hu, Ling Jian, and Huan Liu. ``Toward Time-Evolving Feature Selection on Dynamic Networks", In Proceedings of the 2016 IEEE International Conference on Data Mining (ICDM), 2016.

• Kewei Cheng, ``Mining Signed Social networks Using Unsupervised Learning Algorithms" (Master Thesis)

Related Links

• Survey on Signed Social Networks
• Jiliang Tang, Yi Chang, Charu Aggarwal, and Huan Liu. "A Survey of Signed Network Mining in Social Media", ACM Computing Surveys 49(3): 42:1-42:37, 2016.

• Books on Social Media Mining
• Zafarani, Reza, Mohammad Ali Abbasi, and Huan Liu. "Social Media Mining: An Introduction'', Cambridge University Press, 2014.
• Jiliang Tang and Huan Liu. "Trust in Social Media", Morgan & Claypool Publishers, September, 2015
• Huiji Gao and Huan Liu. "Mining Human Mobility in Location-Based Social Networks", Morgan & Claypool Publishers, April, 2015
• Shamanth Kumar, Fred Morstatter, and Huan Liu. "Twitter Data Analytics", preprint for free download with code, Springer, January, 2014.
• Geoffrey Barbier, Zhuo Feng, Pritam Gundecha, and Huan Liu. "Provenance Data in Social Media", Morgan & Claypool Publishers, May, 2013.
• Lei Tang and Huan Liu. "Community Detection and Mining in Social Media", Morgan & Claypool Publishers, September 2010.
• Nitin Agarwal and Huan Liu. " Modeling and Data Mining in Blogosphere ", Morgan & Claypool Publishers, July 2009.

Resources

• Related Codes
• Feature Selection Repository

• Related Datasets
• Signed social networks from Stanford Large Network Dataset Collection (SNAP)

Project Members

• Huan Liu (Professor)
• Jundong Li (PhD Student)
• Suhang Wang (PhD Student)
• Kewei Cheng (Master Student, Graduated)

Acknowledgments

Created by Huan Liu who can be reached at huan.liu at asu.edu.
Webmaster: Jundong Li, Email: jundongl at asu.edu

Last Upadted: Feb 20th, 2018