Wenjie Zhang – författare

This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.

The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas.

This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.

This book constitutes the refereed proceedings of three workshops held at the 18th Asia-Pacific Web Conference, APWeb 2016, in Suzhou, China, in September 2016: the Second International Workshop on Web Data Mining and Applications, WDMA 2016, the First International Workshop on Graph Analytics and Query Processing, GAP 2016, and the First International Workshop on Spatial-temporal Data Management and Analytics, SDMA 2016.

The 27 full papers included in this book were carefully reviewed and selected from 37 submissions. The goal of these workshops is to promote the new research directions and applications, especially on web data, graph data, and spatial-temporal data management and analytics.

This proceedings constitutes selected papers from the Workshops KGMA, SemiBDMA, MADM, AIEDM and STBDM which were held in conjunction with APWeb-WAIM 2024 which took place in Jinhua, China, August 30–September 1, 2024.

The 28 full papers presented were carefully reviewed and selected from 49 submissions. the five workshops accepted a total of 28 papers, marking an acceptance rate of 57.14%. Each submission has been peer-reviewed by 3-4 reviewers. The five workshops were as follows:

1. The 7th International Workshop on Knowledge Graph Management and Applications (KGMA 2024).

2. The 6th International Workshop on Semi-structured Big Data Management and Applications (SemiBDMA 2024).

3. The 1st International Workshop on Mobile Applications and Data Management (MADM 2024).

4. The 1st International Workshop on Artificial Intelligence in Education and Educational Data Mining (AIEDM 2024).

5. The 1st International Workshop on Spatio-Temporal Big Data Management (STBDM 2024).

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 30–September 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.

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 30–September 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.

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 30–September 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.

The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed conference proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30–September 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:Volume I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System.

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

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

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

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