EvolGCN: A Co-Evolutionary Graph Convolutional Network Model for Dynamically Spatio-Temporal Anomaly Event Inference

Xiaoming Liu, Hang Pu, Zhuo Chen, Zhanwei Zhang, Yu Lan, Chao Shen

Abstract: Accurately spatio-temporal anomaly event inference is significant to enhance society’s safety, such as crime prevention and traffic collision reduction, etc. However, it is hard to achieve good performance for its complicated process being influenced by various kinds of factors. Previous works mainly focus on employing feature-based regression or fitting models with supposed spatio-temporal distribution to tackle this challenge, but are normally short of the following considerations: 1) mutual evolutionary influence, i.e., the dynamic evolution of interactions and dependencies among anomaly events, is changing along with the timeline and alters the probabilities or patterns of their occurrences dynamically; 2) messy features, i.e., complex attributes in the data but with noise, are difficult to select and aggregate for the representation learning algorithm with uncertainty and redundancy. To address the research gap, we put forward a co-evolutionary graph convolutional network model to explicate the dynamically spatio-temporal anomaly patterns. Specifically, we firstly take advantage of a fuzzy-rough set-based algorithm to select features by discovering the specialty and permanence attributes of different interaction features. Then, we propose a co-evolutionary learning method to embed the dynamically temporal influence into latent features with selected interaction information. Finally, we design a graph convolutional network with an attention mechanism to formulate the mutually spatial effects among the anomaly events. The proposed model is verified on the New York City crime records from the real-world, and extensive experiments show that our approach achieves 0.10129, 0.09958 and 0.10034 of the MAE (hour) in the action, location, and action-location time inference tasks, and 0.7973 and 0.4678 of the accuracy in the action and location type inference tasks, which outperform state-of-arts by 24.00%, 50.60%, 11.10%, 10.56% and 21.53% at most, respectively. Hyper-parameter and ablation experiments are also carried out further to demonstrate the sensitivity and effectiveness of our model.

External IDs:dblp:journals/tdsc/LiuPCZLS25