It’s my pleasure to announce that I’ll be giving a hands-on tutorial on social web intelligence in February 21st at UBC.
Organized by the SFU/UBC Salon Series on Digital Social Sciences, Humanities and the Arts, the tutorial/workshop will introduce the theory and practice of basic concepts in network analysis, machine learning, and data mining to make sense of the social and information networks that have been fuelled and rendered accessible by the Internet.
Participants will learn about the structure and evolution of networks, drawing on knowledge from disciplines as diverse as sociology, mathematics, statistics, computer science, economics, and physics.
Interactive demonstrations and hands-on analysis of real-world data sets will focus on a range of tasks: from online network data collection, to identifying important nodes in the network, to detecting communities, to opinion mining and sentiment analysis, to predicting future relationships and social attributes.
For event details and RSVP, click here. For the iPython Notebook (slides), click here.
Our latest research on identifying automated fake accounts in online social networks has been accepted at the 2015 Network and Distributed System Security Symposium (NDSS’15), to be held in Feb in San Diego, USA.
In this work, we present Integro, a scalable defense system that helps OSNs detect fake accounts using a meaningful user ranking scheme. We implemented Integro using Mahout and Giraph in which it scaled nearly linearly. We evaluated Integro against SybilRank, the state-of-the-art in fake account detection, using real-world datasets and a large-scale deployment at Tuenti, the largest OSN in Spain. In particular, we show that Integro significantly outperforms SybilRank in user ranking quality. Moreover, the deployment of Integro at Tuenti resulted in an order of magnitude higher fake account detection precision, as compared to SybilRank.
Integro is published as part of Grafos ML, a system and tools for large-scale machine learning and graph analytics on top of Giraph.
This Summer, I’ll be interning at Microsoft Research Silicon Valley under the kind supervision of Mihai Budui. I’ll be working on interactive big data analytics focusing on systems support for exploratory data analysis and feature engineering. I’m really excited about this opportunity and look forward to working with Mihai and MSR folks!