All Outputs (4)

Ego-graph replay based continual learning for misinformation engagement prediction (2022)
Conference Proceeding
Bo, H., McConville, R., Hong, J., & Liu, W. (2022). Ego-graph replay based continual learning for misinformation engagement prediction. In 2022 International Joint Conference on Neural Networks (IJCNN) (01-08). https://doi.org/10.1109/IJCNN55064.2022.9892557

Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinformation detection systems to classify if a post is misinformation. Instead of pos... Read More about Ego-graph replay based continual learning for misinformation engagement prediction.

Ego-graph replay based continual learning for misinformation engagement prediction (2022)
Conference Proceeding
Bo, H., Mcconville, R., Hong, J., & Liu, W. (in press). Ego-graph replay based continual learning for misinformation engagement prediction. . https://doi.org/10.48550/arXiv.2207.12105

Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinfor-mation detection systems to classify if a post is misinformation. Instead of po... Read More about Ego-graph replay based continual learning for misinformation engagement prediction.

Social influence prediction with train and test time augmentation for graph neural networks (2021)
Conference Proceeding
Bo, H., McConville, R., Hong, J., & Liu, W. (2021). Social influence prediction with train and test time augmentation for graph neural networks. In Proceedings of the International Joint Conference on Neural Networks 2021 (IJCNN 2021)https://doi.org/10.1109/IJCNN52387.2021.9533437

Data augmentation has been widely used in machine learning for natural language processing and computer vision tasks to improve model performance. However, little research has studied data augmentation on graph neural networks, particularly using aug... Read More about Social influence prediction with train and test time augmentation for graph neural networks.

Social network influence ranking via embedding network interactions for user recommendation (2020)
Conference Proceeding
Bo, H., McConville, R., Hong, J., & Liu, W. (2020). Social network influence ranking via embedding network interactions for user recommendation. In WWW '20: Companion Proceedings of the Web Conference 2020 (379-384). https://doi.org/10.1145/3366424.3383299

Within social networks user influence may be modelled based on user interactions. Further, it is typical to recommend users to others. What is the role of user influence in user recommendation In this paper, we first propose to use a node embedding a... Read More about Social network influence ranking via embedding network interactions for user recommendation.