TY - GEN
T1 - Causality Enhanced Societal Event Forecasting with Heterogeneous Graph Learning
AU - Deng, Songgaojun
AU - Rangwala, Huzefa
AU - Ning, Yue
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Using observational event data to forecast societal events has been extensively studied in data-driven models. Prior work focuses on correlational analysis and ignores the importance of causal relationships behind events. Understanding the causality of events helps one infer future events by pinpointing potential triggers. In light of complex and dynamic social environments, it is difficult to comprehensively analyze the causes of societal events. In this work, we study the causal relationship between topics and events where topics are extracted from event-related documents. These topics represent probability distributions of words. We introduce a method to discover topics that have a causal effect on future events of interest. Next, we propose a causality-enhanced dynamic heterogeneous graph learning framework where topics, documents, and words are represented as nodes with changing edges. To handle the temporal dependencies of dynamic graphs, we introduce a temporal information learning module that updates node representations based on their evolving context and heterogeneous semantics. We conduct extensive experiments on four real-world datasets and demonstrate the effectiveness of our method in societal event prediction.
AB - Using observational event data to forecast societal events has been extensively studied in data-driven models. Prior work focuses on correlational analysis and ignores the importance of causal relationships behind events. Understanding the causality of events helps one infer future events by pinpointing potential triggers. In light of complex and dynamic social environments, it is difficult to comprehensively analyze the causes of societal events. In this work, we study the causal relationship between topics and events where topics are extracted from event-related documents. These topics represent probability distributions of words. We introduce a method to discover topics that have a causal effect on future events of interest. Next, we propose a causality-enhanced dynamic heterogeneous graph learning framework where topics, documents, and words are represented as nodes with changing edges. To handle the temporal dependencies of dynamic graphs, we introduce a temporal information learning module that updates node representations based on their evolving context and heterogeneous semantics. We conduct extensive experiments on four real-world datasets and demonstrate the effectiveness of our method in societal event prediction.
KW - Causality
KW - Event Forecasting
KW - Heterogeneous Graph Learning
UR - http://www.scopus.com/inward/record.url?scp=85147731952&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147731952&partnerID=8YFLogxK
U2 - 10.1109/ICDM54844.2022.00019
DO - 10.1109/ICDM54844.2022.00019
M3 - Conference contribution
AN - SCOPUS:85147731952
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 91
EP - 100
BT - Proceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022
A2 - Zhu, Xingquan
A2 - Ranka, Sanjay
A2 - Thai, My T.
A2 - Washio, Takashi
A2 - Wu, Xindong
T2 - 22nd IEEE International Conference on Data Mining, ICDM 2022
Y2 - 28 November 2022 through 1 December 2022
ER -