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[null,"1.","Record Nr.",null,"UNINA9910568239603321"]
[null,null,"Autore",null,"Fang Yixiang"]
[null,null,"Titolo",null,"Cohesive Subgraph Search Over Large Heterogeneous Information Networks / / by Yixiang Fang, Kai Wang, Xuemin Lin, Wenjie Zhang"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"3-030-97568-1"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (86 pages)"]
[null,null,"Collana",null,"SpringerBriefs in Computer Science, , 2191-5776"]
[null,null,"Disciplina",null,"006.312"]
[null,null,"Soggetti",null,"Information storage and retrieval systems"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Discrete mathematics"]
[{"$nulls":4},"Graph theory"]
[{"$nulls":4},"Information Storage and Retrieval"]
[{"$nulls":4},"Discrete Mathematics in Computer Science"]
[{"$nulls":4},"Graph Theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (pages 65-74)."]
[null,null,"Nota di contenuto",null,"Introduction -- Preliminaries -- CSS on Bipartite Networks -- CSS on Other General HINs -- Comparison Analysis -- Related Work on CSMs and solutions -- Future Work and Conclusion."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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."]