![]() Internet users have suffered from phishing attacks for a long time. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20% and 22% of improvement of AUC and AP scores, respectively. We conduct extensive experiments on four real datasets. A triplet embedding is then derived to complete the prediction. ![]() Particularly, we present a concatenation node embedding method by considering the node types and a dynamic meta-path embedding method that carefully considers the importance and positions of edge types in the meta-paths by the Attention mechanism and BiLSTM model, respectively. To address these issues, in this article, we propose a novel prediction model, called ABLE, by exploiting the Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim at predicting the existence of an instance of P. , the meta-path prediction aims at predicting whetherīy an instance of P. Given a heterogeneous information network (HIN) H, a head node
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June 2023
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