Web and Big Data

The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30September 1, 2024.

The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions.

The papers are organized in the following topical sections:
Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System.

Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data.

Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization.

Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security

Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.

Inhalt

.- Spatial and Temporal Data.
.- Temporalformer: A Temporal Decomposition Causal Transformer Network For Wind Power Forecasting.
.- MSCFNet: A Multi-Scale Spatial and Channel Fusion Network for Geological Environment Remote Sensing Interpreting.
.- TS-HCL: Hierarchical Layer-wise Contrastive Learning for Unsupervised Domain Adaptation on Time-Series.
.- Dynamic-Static Fusion for Spatial-Temporal Anomaly Detection and Interpretation in Multivariate Time Series.
.- MFCD:A deep learning method with fuzzy clustering for time series anomaly detection.
.- Graph Neural Network.
.- SBGMN: A Multi-View Sign Prediction Network for Bipartite Graphs.
.- Product Anomaly Detection on Heterogeneous Graphs with Sparse Labels.
.- Generic and Scalable Detection of Risky Transactions Using Density Flows: Applications to Financial Networks.
.- Attributed Heterogeneous Graph Embedding with Meta-graph Attention.
.- Automated Multi-scale Contrastive Learning with Sample-awareness for Graph Classification.
.- CGAR: A Contrastive Graph Attention Residual Network for Enhanced Fake News Detection.
.- GCH: Graph contrastive Learning with Higher-order Networks.
.- LPRL-GCNN for Multi-Relation Link Prediction in Education.
.- Multi-view Graph Neural Network for Fair Representation Learning.
.- MERGE: Multi-View Relationship Graph Network for Event-Driven Stock Movement Prediction.
.- Relation-Aware Heterogeneous Graph Neural Network for Fraud Detection.
.- Graph Mining.
.- Robust Local Community Search over Large Heterogeneous Information Networks.
.- Community discovery in social network via dual-technique.
.- CSGTM: Capsule Semantic Graph-Guided Latent Community Topics Discovery.
.- Efficient (, , )-Core Search in Bipartite Graphs Based on Bi-triangles.
.- Identifying Rank-happiness Maximizing Sets under Group Fairness Constraints.
.- Reachability-Aware Fair Influence Maximization.
.- Towards Efficient Heuristic Graph Edge Coloring.
.- Tree and Graph based Two-Stages Routing for Approximate Nearest Neighbor Search.
.- Unbiasedly Estimate Temporal Katz Centrality and Identify Top-K Vertices in Streaming Graph.
.- Database System and Query Optimization.
.- Gar++: Natural Language to SQL Translation with Efficient Generate-and-Rank.
.- A Composable Architecture for Cloud Transactional DBMS.
.- Computing Minimum Subset Repair On Incomplete Data.
.- Flutist: Parallelizing Transaction Processing for LSM-tree-based Relational Database.
.- Poplar: Partially-Ordered Parallel Logging for Lower Isolation Levels.
.- Table Embedding Models Based on Contrastive Learning for Improved Cardinality Estimation.