基于局部数据增强动态图的事件预测

doi:10.11896/jsjkx.221200054

Abstract

Abstract: Event refers to activities that occur in real world at specific time and places.For instance,unrest,violent terrorist attacks,natural disasters and the spread of infectious diseases,will bring great threats and losses to national security and human life.If the occurrence of such events could be predicted more precisely and effectively,the impact of negative events will be minimized,and it is possible to maximize the benefits of the positive events.It is still a very challenging task to predict events accurately.An event prediction method named local augmented temporal-GAT(LAT-GAT) based on graph attention network is proposed in this paper.It uses conditional variational encoders to generate new features,which will be concatenated with the original features to new one,based on neighbors of the current node.With this approach,our model can utilize the propagation structure of events.In addition,the chronological order of events occurrence is considered by our model.The feature of events in last time point is integrated into the output of the neural network in current time.The temporal property of event propagation is exploited through temporal data integration.And finally,the proposed method is compared with a number of representative baseline me-thods on the real-world datasets,including Thailand,India,Egypt and Russia.The results show that LAT-GAT has the best F1 scores in all datasets.The recall of our model exceeds that of any other baseline methods in the datasets of Thailand,Russia and India.In Thailand,Egypt and India,our model achieves the best precision.Ablation experiments are also conducted to investigate the influence of the model parameters on the final results.

Key words: Event prediction, Graph attention network, Dynamic graph, Conditional variational auto-encoder, Data augmentation

CLC Number:

TP391

PAN Lei, LIU Xin, CHEN Junyi, CHENG Zhangtao, LIU Leyuan, ZHOU Fan. Event Prediction Based on Dynamic Graph with Local Data Augmentation[J].Computer Science, 2024, 51(3): 118-127.

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