A Graph Theory Based Method to Extract Social Structure in the Society

Rafique, Wajid; Khan, Maqbool; Sarwar, Nadeem; Sohail, Muhammad Imran; Irshad, Asma

doi:10.1007/978-981-13-6052-7_38

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Graph sequence databases are found in several domains such as in social networks and gene networks. For example, in a social network, each person may have a graph sequence representing the evolution of its relationships with others (Bonchi, Gionis, Berlingerio, & Bjorn, 2017;Richter, Kelly, Haugen, & Flores, 2019;Sohail & Irshad, 2018).…”

Section: Mining Patterns In a Graph Sequence Databasementioning

confidence: 99%

A survey of pattern mining in dynamic graphs

Fournier-Viger

Cheng

et al. 2020

WIREs Data Min & Knowl

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Graph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities.

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Section: Mining Patterns In a Graph Sequence Databasementioning

confidence: 99%

A survey of pattern mining in dynamic graphs

Fournier-Viger

Cheng

et al. 2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…A sonar system's target strength metric can be used to estimate the size of a sonar target. Submarine exteriors are frequently coated with substances that absorb signals that have been backscattered from them [20]. The use of synthetic aperture sonar (SAS), as demonstrated by references [21][22], represents an intriguing possibility to greatly improve the accuracy and detail of target strength modelling.…”

Section: Introductionmentioning

confidence: 99%

Submarine Acoustic Target Strength Modeling at High-Frequency Asymptotic Scattering

Kumar,

Chinthaginjala,

Anbazhagan

et al. 2024

IEEE Access

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The ability to identify asymptotic scattering is critical for the continuous operation of modern combat systems. In the active sonar equation, acoustic target strength is crucial. The plane-wave propagation target strength equation predicts far-field reradiated intensity. Submarines defend themselves by being invisible. Sonar can be used to detect submarines. Saltwater readily absorbs radio waves. Commercial fishing and scientific ocean exploration both make use of sonar technology. Acoustic wave reflection reduces the area requirements of ocean depth sections, therefore submarine designers take this into consideration. The Target Strength (TS) of a sonar target is used to assess its size. The Pressure Acoustic-Boundary Element Model (PA-BEM) of the High-Frequency Boundary Model (HFB) illuminates and simplifies the TS analysis of the Benchmark Target Echo Strength Simulation (BeTTSi) benchmark submarine. We explore how subsea shape, material qualities, and operation frequency affect acoustic target strength using comprehensive models. The findings underline the need of correctly characterising the structural sections of the submarine and their impact on dispersion. They also help with research into the submarine's acoustic signature, detectability, and potential detection mitigation measures.

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DTSSN: A Distributed Trustworthy Sensor Service Network Architecture for Smart City

Pang,

Luo,

Zhao

et al. 2024

ACM Trans. Sen. Netw.

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The smart city is an increasingly popular concept when it comes to urban development. In a smart city, numerous sensor services are generated by IoT sensors in a distributed manner, requiring proper management and effective interaction to guarantee the connectivity of different regions. However, the sensitive nature of sensor data raises concerns over joining public cloud centers or edge servers, despite assurances of their reliability from providers. Local deployment and maintenance of sensor services may cause these service providers to become ”data isolated islands”, hindering the construction process of smart city. This paper proposes a distributed trustworthy sensor service network architecture named DTSSN to support the building of a fully distributed sensor service network. The proposed network architecture operates through the collaboration of two core devices, the sensor service switch and router, to effectively enable the registration, discovery, invocation, transaction, and monitoring of cross-region sensor services. Then, a lightweight trustworthy transaction mechanism based on blockchain is proposed to realize SLA-based automatic service transaction while reducing potential risks in the service network. Comparative analysis and simulation experiments validate the effectiveness of the DTSSN architecture in terms of scalability, availability, and trustworthiness, underscoring its potential in advancing smart city development and governance.

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A Graph Theory Based Method to Extract Social Structure in the Society

Cited by 6 publications

References 12 publications

A survey of pattern mining in dynamic graphs

A survey of pattern mining in dynamic graphs

Submarine Acoustic Target Strength Modeling at High-Frequency Asymptotic Scattering

DTSSN: A Distributed Trustworthy Sensor Service Network Architecture for Smart City

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