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

書目名稱Web and Big Data影響因子(影響力)




書目名稱Web and Big Data影響因子(影響力)學(xué)科排名




書目名稱Web and Big Data網(wǎng)絡(luò)公開度




書目名稱Web and Big Data網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Web and Big Data被引頻次




書目名稱Web and Big Data被引頻次學(xué)科排名




書目名稱Web and Big Data年度引用




書目名稱Web and Big Data年度引用學(xué)科排名




書目名稱Web and Big Data讀者反饋




書目名稱Web and Big Data讀者反饋學(xué)科排名

疲勞

,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

無(wú)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

聾子

,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

小爭(zhēng)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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