Notorious
发表于 2025-3-23 10:47:08
MANE: A Multi-cascade Adversarial Network Embedding Model for Anchor Link Predictions for correspondence matching. Extensive experiments on real-world social network datasets demonstrate that our method can achieve the expected performance, especially in improving the top-1 precision and recall.
inclusive
发表于 2025-3-23 14:58:51
http://reply.papertrans.cn/29/2845/284468/284468_12.png
Bucket
发表于 2025-3-23 20:27:30
GPSR: Graph Prompt for Session-Based Recommendation. Specifically, we first study the item transition pattern by constructing session graphs, based on which the GNN model is pretrained. Then, we introduce a universal prompt-based tuning method called Graph Prompt Feature (GPF), for adapting the pretrained GNN model to the downstream session-based re
铺子
发表于 2025-3-24 01:28:12
http://reply.papertrans.cn/29/2845/284468/284468_14.png
INERT
发表于 2025-3-24 03:07:35
Global Route Planning for Large-Scale Requests on Traffic-Aware Road Networkgroup them together. In this way, only the conflicts within each group need to be resolved in a local area, so the efficiency is improved. Additionally, several alternative paths are calculated and the global optimal routes are found in finite iterations. Extensive experiments conducted on real-worl
打火石
发表于 2025-3-24 10:05:58
http://reply.papertrans.cn/29/2845/284468/284468_16.png
冒烟
发表于 2025-3-24 11:59:04
http://reply.papertrans.cn/29/2845/284468/284468_17.png
打包
发表于 2025-3-24 17:51:03
http://reply.papertrans.cn/29/2845/284468/284468_18.png
联想
发表于 2025-3-24 19:09:09
,Verzweigter Stromübertritt in die Erde,. Specifically, We use LightGCN to learn user and item embeddings, and then we combine multi-task learning with contrastive learning to explicitly exploit behavioral dependence in embeddings learning and capture differences between embeddings. We conduct comprehensive experiments on two real-world d
Emg827
发表于 2025-3-25 00:01:13
https://doi.org/10.1007/978-3-662-41795-9ified heterogeneous graph, creating the heterogeneous view. We also construct the social relation enhanced view by resampling the user-item interaction graph. In the learning process, we leverage meta-path based graph learning and graph diffusion with attention to obtain multi-view embeddings for us