Titlebook: Web and Big Data; 7th International Jo Xiangyu Song,Ruyi Feng,Geyong Min Conference proceedings 2024 The Editor(s) (if applicable) and The

JOLT

书目名称Web and Big Data影响因子(影响力)




书目名称Web and Big Data影响因子(影响力)学科排名




书目名称Web and Big Data网络公开度




书目名称Web and Big Data网络公开度学科排名




书目名称Web and Big Data被引频次




书目名称Web and Big Data被引频次学科排名




书目名称Web and Big Data年度引用




书目名称Web and Big Data年度引用学科排名




书目名称Web and Big Data读者反馈




书目名称Web and Big Data读者反馈学科排名

疲劳

,MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection, due to both the class imbalance issue and the camouflaged behaviors of anomalous nodes. Recently, some graph contrastive learning (GCL) methods have been proposed to solve the above issue. However, local aggregation-based GNN encoders can not consider the long-distance nodes, leading to over-smooth

Microgram

,YOLO-SA: An Efficient Object Detection Model Based on Self-attention Mechanism,ally adopts complex multi-branch design, which reduces the reasoning speed and memory utilization. Moreover, in many works, attention mechanism is usually added to the object detector to extract rich features in spatial information, which are usually used as additional modules of convolution without

无法治愈

,Detecting Critical Nodes in Hypergraphs via Hypergraph Convolutional Network,e and natural modeling tool to model such complex relationships. Detecting the set of critical nodes that keeps the hypergraph structure cohesive and tremendous has great significance. At present, all the researches in detecting critical nodes area focus on traditional pairwise graphs, and how to ex

consolidate

,Retrieval-Enhanced Event Temporal Relation Extraction by Prompt Tuning,t-oriented natural language understanding and generation. For this task, impressive improvements have been made in neural network-based approaches. However, they typically treat it as a supervised classification task and inevitably suffer from under-annotated data and label imbalance problems. In th

BAIT

,Adaptive Label Cleaning for Error Detection on Tabular Data,n algorithms ignore the harm of noisy labels to detection models. In this paper, we design an effective approach for error detection when both data values and labels may be noisy. Nevertheless, we present AdaptiveClean, a method for error detection on tabular data with noisy training labels. We intr

聋子

,MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection, due to both the class imbalance issue and the camouflaged behaviors of anomalous nodes. Recently, some graph contrastive learning (GCL) methods have been proposed to solve the above issue. However, local aggregation-based GNN encoders can not consider the long-distance nodes, leading to over-smooth

小争吵

,Detecting Critical Nodes in Hypergraphs via Hypergraph Convolutional Network,e and natural modeling tool to model such complex relationships. Detecting the set of critical nodes that keeps the hypergraph structure cohesive and tremendous has great significance. At present, all the researches in detecting critical nodes area focus on traditional pairwise graphs, and how to ex

Ledger

,A Dual−Population Strategy Based Multi−Objective Yin−Yang−Pair Optimization for Cloud Computing,per proposes a novel Dual−Population strategy based Multi−Objective Yin−Yang−Pair Optimization which is termed as DP−MOYYPO. The proposed DP−MOYYPO algorithm makes the following three improvements to Front−based Yin−Yang−Pair Optimization (F−YYPO). First, a population of the same size to explore non

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