Yue Ning, Rongrong Tao, Chandan Reddy, Naren Ramakrishnan
Large-scale societal events such as civil unrest movements occur due to a variety of factors including economics, politics, and security. Societal event detection can be modeled as a system of inter-connected locations, where each location is recording a set of time-dependent observations. In order to detect event occurrence and automatically reconstruct the precursors and signals, it is essential to model relationships between the different locations w.r.t. how events evolve over time. However, existing methods for precursor discovery do not capture or exploit spatial and temporal correlations inherent in event occurrences. The absence of such modeling not only creates shortcomings in the quality of inference but also curtails interpretation by human analysts. Furthermore, forecasting is inhibited when training data is sparse. In this paper, we develop a novel multi-task model with dynamic graph constraints within a multi-instance learning framework. Our model tackles the problem of scarce data distribution and reinforces co-occurring location-specific precursors with augmented representations. Through studies on civil unrest movements in numerous countries, we demonstrate the effectiveness of the proposed method for precursor discovery and event forecasting.
Yue Ning, Rongrong Tao, Chandan K. Reddy , Huzefa Rangwala, James C. Starz, Naren Ramakrishnan: STAPLE: Spatio-Temporal Precursor Learning for Event Forecasting. SDM 2018: 99-107
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- SIAM International Conference on Data Mining
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