Cohesive Subgraph Search Over Large Heterogeneous Information Networks

This SpringerBrief provides a systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs), covering models, algorithms, and comparison studies. It also offers promising future research directions for performing CSS over large HINs. The authors classify existing works and analyze and compare cohesive subgraph models and solutions, focusing on their similarities and differences, computational efficiency, and model properties. The SpringerBrief is targeted at researchers, professors, engineers, and graduate students in graph data management and graph mining.

Format: Paperback / softback
Length: 74 pages
Publication date: 07 May 2022
Publisher: Springer Nature Switzerland AG

This comprehensive SpringerBrief presents the first systematic review of the existing literature on cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It delves into the latest research breakthroughs in this field, encompassing models, algorithms, and comparison studies conducted in recent years. Additionally, the brief offers a insightful exploration of promising future research directions for CSS operations on large HINs.

The authors commence by categorizing the existing works of CSS over HINs based on classical cohesiveness metrics, including core, truss, clique, connectivity, density, and more. They then provide an extensive review of the specific models and their corresponding search solutions within each classification. It is noteworthy that since bipartite networks are a special case of HINs, models developed for general HINs can be directly applied to bipartite networks. However, models tailored for bipartite networks may not be readily extended to other general HINs due to their distinct settings.

Furthermore, the authors meticulously analyze and compare these cohesive subgraph models (CSMs) and their corresponding solutions. They examine various perspectives, such as the cohesiveness constraints, shared properties, and computational efficiency, to gain a deeper understanding of the similarities and differences between different groups of CSMs. Additionally, the authors delve into the model properties and high-level algorithm ideas of each group of CSMs, providing valuable insights for researchers, professors, engineers, and graduate students engaged in the fields of graph data management and graph mining.

This SpringerBrief is particularly relevant to researchers, professors, engineers, and graduate students who are actively involved in the study and application of graph data management and graph mining. Undergraduate students majoring in computer science, databases, data and knowledge engineering, and data science will also find this brief to be an invaluable resource for their academic pursuits.

Weight: 163g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030975678
Edition number: 1st ed. 2022