Communal
发表于 2025-3-26 21:09:47
Xugang Wu,Huijun Wu,Ruibo Wang,Duanyu Li,Xu Zhou,Kai Luxamples on M/G/1 queues, and a new section on G/M/1 queues. Additionally, there are two other important new sections: on the level-crossing derivation of the finite time-t probability distributions of excess, 978-3-319-84375-9978-3-319-50332-5Series ISSN 0884-8289 Series E-ISSN 2214-7934
出血
发表于 2025-3-27 05:08:52
Learning to Augment Graph Structure for both Homophily and Heterophily Graphsion and label distribution information in the graph structure to further reduce the reliance on annotated labels and improve applicability to heterophily graphs. Extensive experiments have shown that L2A can produce truly encouraging results at various homophily levels compared with other leading me
Germinate
发表于 2025-3-27 07:39:53
Learning Representations for Bipartite Graphs Using Multi-task Self-supervised Learningglobal information. We utilize deep multi-task learning (MTL) to further assist in learning generalizable self-supervised solution. To mitigate negative transfer when related and unrelated tasks are trained in MTL, we propose a novel DST++ algorithm. The proposed DST++ optimization algorithm improve
露天历史剧
发表于 2025-3-27 10:47:43
Multi-label Image Classification with Multi-scale Global-Local Semantic Graph Networkation between global information and local features in multi-scale features, which using the way of adaptive cross-fusion to locate the target area more accurately. Moreover, we propose the multi-perspective weighted cosine measure in multi-perspective dynamic semantic representation module to const
discord
发表于 2025-3-27 15:45:43
CasSampling: Exploring Efficient Cascade Graph Learning for Popularity Predictionlobal propagation time flow. Then, we design an attention aggregator for node-level representation to better integrate local-level propagation into the global-level time flow. Experiments conducted on two benchmark datasets demonstrate that our method significantly outperforms the state-of-the-art m
Pelvic-Floor
发表于 2025-3-27 21:01:39
Boosting Adaptive Graph Augmented MLPs via Customized Knowledge Distillationhe guided knowledge to mitigate the adverse influence of heterophily to student MLPs. Then, we introduce an adaptive graph propagation approach to precompute aggregation feature for node considering both of homophily and heterophily to boost the student MLPs for learning graph information. Furthermo
Deceit
发表于 2025-3-28 01:57:27
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learningof node importance in representation learning. Then, we design two data augmentation schemes on graphs for perturbing structural and feature information, respectively. We also provide justification for the proposed method in the framework of information theories. Experiments of both graph-level and
LEVY
发表于 2025-3-28 03:44:43
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出价
发表于 2025-3-28 08:19:16
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hermitage
发表于 2025-3-28 14:18:54
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphsd the student models learn from each other to improve their overall performance. Experiments in eight node classification benchmarks in both transductive and inductive settings showcase . ’s superiority over existing distillation approaches for textual graphs. Our code and supplementary material are