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Top-k Similarity Search in HINs by s358695177 is a document available to read on EtoBox.
This article presents a novel approach for top-k similarity search in weighted heterogeneous information networks (HINs) using a double channel convolutional neural network (SSDCC?). The model integrates both structural and content information through distinct attention mechanisms, enhancing the accuracy and explainability of the search results. Experimental results demonstrate that this method outperforms existing approaches in effectively supporting similarity searches in HINs.
- Author
- s358695177
- Language
- EN