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State-of-the-art Graph Neural Networks (GNNs) have achieved tremendoussuccess in social event detection tasks when restricted to a closed set ofevents. However, considering the large amount of data needed for training aneural network and the limited ability of a neural network in handlingpreviously unknown data, it remains a challenge for existing GNN-based methodsto operate in an open set setting. To address this problem, we design aQuality-aware Self-improving Graph Neural Network (QSGNN) which extends theknowledge from known to unknown by leveraging the best of known samples andreliable knowledge transfer. Specifically, to fully exploit the labeled data,we propose a novel supervised pairwise loss with an additional orthogonalinter-class relation constraint to train the backbone GNN encoder. The learnt,already-known events further serve as strong reference bases for the unknownones, which greatly prompts knowledge acquisition and transfer. When the modelis generalized to unknown data, to ensure the effectiveness and reliability, wefurther leverage the reference similarity distribution vectors for pseudopairwise label generation, selection and quality assessment. Following thediversity principle of active learning, our method selects diverse pair sampleswith the generated pseudo labels to fine-tune the GNN encoder. Besides, wepropose a novel quality-guided optimization in which the contributions ofpseudo labels are weighted based on consistency. We thoroughly evaluate ourmodel on two large real-world social event datasets. Experiments demonstratethat our model achieves state-of-the-art results and extends well to unknownevents.

From Known to Unknown: Quality-aware Self-improving Graph Neural Network for Open Set Social Event Detection