Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric

Kanavos, Andreas; Drakopoulos, Georgios; Tsakalidis, Athanasios K.

doi:10.5220/0006382104030410

Cited by 20 publications

(9 citation statements)

References 20 publications

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“…For an overview of recent security practices for mobile health applications see Papageorgiou et al (2018). Path analysis as in Kanavos et al (2017) play a central role in graph mining in various contexts, for instance in social networks as in Drakopoulos et al (2017). Finally, the advent of advanced GPU technologies can lead to more efficient graph algorithms as in Drakopoulos et al (2018).…”

Section: Previous Workmentioning

confidence: 99%

An Architecture For Cooperative Mobile Health Applications

Drakopoulos,

Mylonas,

Sioutas

2023

Preprint

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Mobile health applications are steadily gaining momentum in the modern world given the omnipresence of various mobile or WiFi connections. Given that the bandwidth of these connections increases over time, especially in conjunction with advanced modulation and error-correction codes, whereas the latency drops, the cooperation between mobile applications becomes gradually easier. This translates to reduced computational burden and heat dissipation for each isolated device at the expense of increased privacy risks. This chapter presents a configurable and scalable edge computing architecture for cooperative digital health mobile applications.

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Section: Previous Workmentioning

confidence: 99%

An Architecture For Cooperative Mobile Health Applications

Drakopoulos,

Mylonas,

Sioutas

2023

Preprint

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“…Practical ways and the associated challenges to implement a blockchain over IoT and edge computing are shown in Zyskind et al (2015). The financial prospects of Bitcoin in terms of wealth accumulation as well as the properties of Bitcoin versus the traditional fiat currency are the focus of a number of works, for instance Antonopoulos (2014), Antonopoulos (2017), Kosba et al (2016), Swan (2015), and Böhme et al (2015). The distributed implementation of blockchains is discussed in Abbas et al (2018) and in Pass et al ( 2017), whereas security aspects of the blockchains are treated in Puthal et al (2018).…”

Section: Previous Workmentioning

confidence: 99%

Blockchain For Mobile Health Applications: Acceleration With GPU Computing

Drakopoulos,

Marountas,

Liapakis

et al. 2023

Preprint

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Blockchain is a linearly linked, distributed, and very robust data structure. Originally proposed as part of the Bitcoin distributed stack, it found a number of applications in a number of fields, most notably in smart contracts, social media, secure IoT, and cryptocurrency mining. It ensures data integrity by distributing strongly encrypted data in widely redundant segments. Each new insertion requires verification and approval by the majority of the users of the blockchain. Both encryption and verification are computationally intensive tasks which cannot be solved with ordinary off-the-shelf CPUs. This has resulted in a renewed scientific interest in secure distributed communication and coordination protocols. Mobile health applications are growing progressively popular and have the enormous advantage of timely diagnosis of certain conditions. However, privacy concerns have been raised as mobile health application by default have access to highly sensitive personal data. This chapter presents concisely how blockchain can be applied to mobile health applications in order to enhance privacy.

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“…En el análisis de las redes sociales el descubrimiento de comunidades es esencial. Por ello, se implementan algoritmos para el descubrimiento de comunidades en Neo4j, evaluando los resultados obtenidos a través de la comparación de métricas de similitud (Kanavos et al, 2017). En este contexto, la descomposición de forma recursiva de un grafo permite el análisis de una red social, evidenciando que el algoritmo de detección de comunidades Louvain crea comunidades mejor distribuidas (Neo4j, 2020a).…”

Section: Estado Del Arteunclassified

Analítica de grafos para identificar entidades relevantes y comunidades en Mercado Libre: un estudio de caso

Gelvez

Galpin

2021

Revista Mutis

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Este artículo representa la información disponible en bases de datos no relacionales, aprovechando los beneficios de escalabilidad, alta disponibilidad, resiliencia y facilidad proporcionados por estas. Así mismo, se da a conocer una serie de algoritmos suministrados por el motor de bases de datos de grafos Neo4j para computar métricas de grafos, nodos y relaciones. En primer lugar, se consolida un conjunto de datos públicos tomado del sistema de ventas online de Mercado Libre. Posteriormente, se modelan los datos obtenidos en un esquema de grafos que tiene como nodos a los usuarios, quienes pueden ser vendedores, compradores, productos y sus características. Como siguiente paso, se aplican algoritmos que calculan métricas del grafo, junto con sus nodos y relaciones, visualizando de esta manera los resultados obtenidos. Para finalizar, se identifican las categorías ofertadas más importantes, las comunidades existentes y los usuarios más influyentes.

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Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric

Cited by 20 publications

References 20 publications

An Architecture For Cooperative Mobile Health Applications

An Architecture For Cooperative Mobile Health Applications

Blockchain For Mobile Health Applications: Acceleration With GPU Computing

Analítica de grafos para identificar entidades relevantes y comunidades en Mercado Libre: un estudio de caso

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