Graph-based Approaches for Social Media Mining


Senior Engineer
Yahoo! Taiwan




10 December 2013 (Tuesday)

1:00pm - 2:00pm

Seminar Room 2.4, Level 2
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902

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With the emergence of mobile devices in the social media era, tracking user activities from social media in physical world becomes feasible. A user trajectory collected from location-based social media (e.g., Foursquare etc.) is a low-sampling-rate sequence of data points, where each data point corresponds to a check-in record performed with time and location information. Analyzing low-sampling-rate trajectories leads to new requirements for location-based services: managing data sparsity, developing mobility models and efficient processing. Driven by these requirements, we have developed two location-based services: a location prediction model for distant-time queries and a user mobility profiling model from low-sampling-rate trajectories.

First, we address the problem of sparsity in low-sampling-rate trajectories and propose an adaptive temporal exploration approach to retrieve supporting trajectories. These supporting trajectories are further formed into a time-constrained mobility graph. We have designed a Reachability-based prediction model on the Time-constrained Mobility Graph (RTMG) that follows the principle of random walk simulation with the restart from the current location. To support efficient query processing, we have developed an index structure and corresponding operators for efficient query processing. Extensive experiments with real data demonstrate the effectiveness and efficiency of RTMG with adaptive temporal exploration over varying data sparsity.

Second, we formulate the problem of mining mobility dynamics from check-in trajectories for user mobility profiling. Given a check-in trajectory of a user, we aim to divide the trajectories into a sequence of segments, with each segment associated with a time-interval and a group of hot regions where the user appears during this time-interval. Initially, we divide trajectories into segments of equal time units. Then, we define the change sed on the spatial distribution. To measure the segmentation quality, we have designed a quality metric based on the MDL principle. Based on the quality metric, we have developed a family of greedy algorithms to automatically derive a sequence of segments and their corresponding regions and valid time intervals. We have conducted experiments on real datasets to demonstrate the effectiveness of the proposed algorithms.

About the speaker

Dr. Meng-Fen CHIANG is a senior engineer at Yahoo! Taiwan Search Team. Dr. Chiang graduated with PhD in Computer Science in National Chiao-Tung University in the fall 2012. Her advisor was Prof. Wen-Chih Peng. Her research focus is on 1) Web mining, 2) geographical trajectory mining, 3) network analysis. Meng-Fen's recent research focus is geosocial structured data analysis (Flickr), where she has been working with cloud computing technologies, modeling user behaviors, and conducting intensive experiments on real networks.

  LARC is supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office, Media Development Authority (MDA).  

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