Community Detection in Scientific Co-Authorship Networks using Neo4j

Aung, Thet Thet; Nyunt, Thi Thi Soe

doi:10.1109/icca49400.2020.9022826

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…The network has an overall density of 0.0029. To ensure high cohesion and low coupling within the author group, the Louvain algorithm was utilized to detect and group nodes in the collaboration network, aiming to maximize modularity [62]. Figure 2 illustrates authors who have published at least one paper, showing several isolated subnetworks where authors tend to collaborate in small teams with limited communication.…”

Section: Author Collaboration Analysismentioning

confidence: 99%

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Trends and Future Directions in Research on the Protection of Traditional Village Cultural Heritage in Urban Renewal

Xia,

Gu,

et al. 2024

Buildings

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The process of urbanization has accelerated economic growth while also presenting social challenges. Urban renewal is crucial for achieving sustainable urban development, especially by preserving traditional villages as cultural heritage sites within cities. This study employs Python algorithm programming and visual analysis functions to conduct a bibliometric analysis of 408 research papers on the preservation of traditional village cultural heritage in urban renewal from 1999 to 2023 in the Web of Science core database. The objective is to examine the historical background, current status, and future trends in this area. The analysis explores cooperation networks, co-citation relationships, co-occurrence patterns, and emerging characteristics of research on traditional village cultural heritage protection in urban renewal. It focuses on various aspects, such as authors, institutions, countries, journals, documents, and keywords. The results indicate that the study of traditional village cultural heritage protection in urban renewal can be divided into three developmental stages. “Sustainable development”, “cultural heritage”, “historic urban landscapes”, and “rural revitalization” are the research hotspots and future trends in this field. The results of this study provide a comprehensive overview of the evolution of research hotspots in this field and can help researchers willing to work in this research area quickly understand the research frontiers and the general situation.

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Section: Author Collaboration Analysismentioning

confidence: 99%

“…lized to detect and group nodes in the collaboration network, aiming to maximize modularity [62]. Figure 2 illustrates authors who have published at least one paper, showing several isolated subnetworks where authors tend to collaborate in small teams with limited communication.…”

Section: Institutional Collaboration Analysismentioning

confidence: 99%

Trends and Future Directions in Research on the Protection of Traditional Village Cultural Heritage in Urban Renewal

Xia,

Gu,

et al. 2024

Buildings

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“…Specifically, we compare the OrientDB multi-model database with the Neo4j graph database and the MongoDB document store. We chose OrientDB, as it is currently one of the most popular and advanced multi-model database [7], [8], whereas MongoDB and Neo4j are suitable representatives of document [9] and graph [10] databases. As for the comparison metric, we use the execution time of queries as it is a standard metric for comparison also in other (non-cluster) benchmarks [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB

Macák¹,

Stovcik²,

Bühnová³

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Digitalization is currently the key factor for progress, with a rising need for storing, collecting, and processing large amounts of data. In this context, NoSQL databases have become a popular storage solution, each specialized on a specific type of data. Next to that, the multi-model approach is designed to combine benefits from different types of databases, supporting several models for data. Despite its versatility, a multi-model database might not always be the best option, due to the risk of worse performance comparing to the single-model variants. It is hence crucial for software engineers to have access to benchmarks comparing the performance of multi-model and single-model variants. Moreover, in the current Big Data era, it is important to have cluster infrastructure considered within the benchmarks.In this paper, we aim to examine how the multi-model approach performs compared to its single-model variants. To this end, we compare the OrientDB multi-model database with the Neo4j graph database and the MongoDB document store. We do so in the cluster setup, to enhance state of the art in database benchmarks, which is not yet giving much insight into cluster-operating database performance.

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Knowledge Graphs for Community Detection in Textual Data

Rollo

Po²

2022

Knowledge Graphs and Semantic Web

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Community Detection in Scientific Co-Authorship Networks using Neo4j

Cited by 8 publications

References 8 publications

Trends and Future Directions in Research on the Protection of Traditional Village Cultural Heritage in Urban Renewal

Trends and Future Directions in Research on the Protection of Traditional Village Cultural Heritage in Urban Renewal

How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB

Knowledge Graphs for Community Detection in Textual Data

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