Community Detection Based on Readers' Borrowing Records

Liu, Xin; Haihong, E; Song, Junde

doi:10.1109/greencom-ithings-cpscom.2013.171

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The result is works of Turkish and world literature that are on the reading lists of many readers. Xin et al proposed a community detection-based book recommendation by constructing a similar network of readers [11]. The results of the study, the authors found that most people only borrow one or two books once at a time and the interval between two consecutive borrowing records is usually taken half a month.…”

Section: Fig 1 Communities In a Bipartite Networkmentioning

confidence: 99%

Dealing With Security Issues In IoT

Sreedharan¹

2023

ICoSTEC

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IoT (Internet of Things) is related to design of a lot of iinter-related “contrived" gadgets having finite capacities with regard to memory and processing power and design is like an Internet. These are frequently battery-driven, raises requirement for adopting environment-friendly techniques. Between the noteworthy ultimatum that establishing interrelated smart objects accompanies are uniformity and coordination. Many smart objects are presumed to develop and addresses of IPv4 are normally utilized. IPv6 is recognized as a possibility for smartobject transmission. Internet of Things (IoT) is a global network of physical and virtual ‘things’ connected to the internet. All objects have unique IDs that are utilized for identification. IoT is the emerging technology which can change the way we communicate with devices. Hereafter almost every electronic device will be a smart device that will be able to compute and communicate with handheld and other infrastructure gadgets. The Internet of Things utilization builds a lot of security problems, which arises from • Smart objects essence: utilization of cryptographic algorithms which are delicate, with regard to requirements of processing and memory. • Quality protocols utilization and the requirement to reduce the data quantity swapped among nodes. An IoT usage which accumulates or handles consumer distinctive data like bank particulars require a foremost rank of security, while a sensor linked to a station area will have a low rank. It need not be costly to carry out fundamental security steps; but it is significant to determine a security strategy and incorporate recuperation measures in the security problems incidents. The main aim of this topic is to describe mechanisms which can help to deal with IoT security issues.

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Section: Fig 1 Communities In a Bipartite Networkmentioning

confidence: 99%

Dealing With Security Issues In IoT

Sreedharan¹

2023

ICoSTEC

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“…Keterbatasan penelitian ini adalah belum dilakukan validasi model terkait evaluasi kinerja. Yassine melakukan penelitian pada repositori Khan untuk mengetahui perkembangan komunitas dan kinerja jaringan pembelajaran online dengan menerapkan algoritma deteksi komunitas Parallel Louvain dan Parallel LPA [6]. Li dkk menganalisis topologi jaringan bipartit peminjaman buku di perpustakaan Universitas Shangai, dan mereka mengusulkan sistem rekomendasi buku menggunakan data jaringan [7].…”

Section: Signifikansi Studi a Studi Literatur 1 Kajian Penelitian Ajs...unclassified

Klasterisasi Buku dan Peminjam Buku di Perpustakaan dengan Metode Analisis Jejaring Sosial dan Deteksi Komunitas

Setiadi¹

2022

ISI

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Abstrack -Book lending is the most important service in the library. So far, book borrowing data is often used as a statistical report, has not been analyzed further to find patterns/knowledge to deepen the insight of library managers. With the rapid growth of big data, social network analysis and community detection have been studied intensively by many researchers over the past few years. However, little research has been done on social network analysis and community detection of borrowing books at the library, and no one has even conducted a comparison analysis of community detection algorithms on book lending. In this paper, we propose an analysis of the library's book borrowing database using social network analysis and community detection methods. The purpose of this study is to find book clusters and borrower clusters by utilizing the best community detection method obtained. The research step begins with collecting data on borrowing books, constructing it into a bipartite graph model, projecting the bipartite graph into a book graph and a book borrowing graph. Then conduct experiments comparing several community detection algorithms for the two graphs, with evaluation metrics in the form of modularity, performance, coverage, density and entropy. The experimental results of Louvain's algorithm and Eva's algorithm have the best performance for book graphs and book borrowers. The application of community detection to the book graph obtained 16 clusters of books, while the book borrower graph obtained 21 clusters of book borrowers. The results of this clustering can be used as recommendations for library management in making library programs to increase the utility of books and increase user loyalty.

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