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.

The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021. The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included. The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic.

This book constitutes the proceedings of the 23nd International Conference on Web Information Systems Engineering, WISE 2021, held in Biarritz, France, in November 2022. The 31 full, 13 short and 3 demo papers were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: Social Media, Spatial & Temporal Issues, Query Processing & Information Extraction, Architecture and Performance, Graph Data Management, Security & Privacy, Information Retrieval & Text Processing, Reinforcement Learning, Learning & Optimization, Spatial Data Processing, Recommendation, Neural Networks, and Demo Papers.

Investigates the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network.

This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.

This book constitutes the refereed proceedings of the 4th International Conference on Spatial Data and Intelligence, SpatialDI 2023, held in Nanchang, China, in April 13–15, 2023. The 18 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: traffic management; visualization analysis; spatial big data analysis; spatiotemporal data mining; spatiotemporal data storage; and metaverse.

The Spatial Grasp Model suggests uses beyond the theoretical, including the examination of hurricanes and forest fires. Investigating group behaviour of ocean animals, discovery of unknown terrain features, and path-findings in large transport networks truly demonstrates the real-world application of SGL.