比喻好
发表于 2025-4-1 03:55:06
GENE: Global Enhanced Graph Neural Network Embedding for Session-Based Recommendationsed on the order in which the items interact in the session with normalization. Second, we employ a graph neural network to obtain the latent vectors of items, then we represent the session graph by attention mechanisms. Third, we explore the session representation fusion for prediction incorporatin
残忍
发表于 2025-4-1 07:20:59
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动机
发表于 2025-4-1 14:13:43
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恃强凌弱
发表于 2025-4-1 15:41:48
Research on Predicting the Impact of Venue Based on Academic Heterogeneous Networkattribute features effectively. To solve the above problems, we propose a hybrid model of academic heterogeneous network representation learning combined with multivariate random walk, termed as AHRV. The specific content is to mine the heterogeneous local network information of nodes in the academi
CALL
发表于 2025-4-1 21:17:06
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omnibus
发表于 2025-4-2 01:53:45
Knowledge Graph Completion with Fused Factual and Commonsense Information based on the analytic hierarchy process. The obtained commonsense can further improve the quality of negative samples and the effectiveness of link prediction. Experimental results on four datasets of the knowledge graph completion (KGC) task show that our method can improve the performance of the original knowledge graph embedding (KGE) model.
Hallowed
发表于 2025-4-2 05:57:34
Finding Introverted Cores in Bipartite Graphss inside focus on the products in the subgraph. We propose an .(.) algorithm to compute the .-core with given . and .. Besides, we introduce an efficient algorithm to decompose a graph by the .-core. The experiments on real-world data demonstrate that our model is effective and our proposed algorithms are efficient.
Aqueous-Humor
发表于 2025-4-2 09:31:28
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Indelible
发表于 2025-4-2 14:30:57
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伪证
发表于 2025-4-2 16:57:34
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