A Proximity Based Community Detection in Temporal Graphs

Advaith, H L; Adarsh, S.; Akshay, G; Sreeram, P.; Sajeev, G P

doi:10.1109/conecct50063.2020.9198394

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Advaith et al [6] proposed a unique technique for community detection in dynamic social networks. Their method converts temporal graphs into static ones while retaining crucial temporal data.…”

Section: A Proximity-based Systems In Communitiesmentioning

confidence: 99%

Development and Validation of a Proximity-based Wearable Computing Testbed for Community-oriented Wearable Systems

Li,

Mangione,

Porreca

et al. 2024

2024 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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In the rapidly evolving digital technology landscape, community-oriented wearable computing systems are emerging as a key tool for enhancing connectivity and interaction within communal spaces. This paper contributes to this burgeoning field by presenting the development and implementation of a proximity-based wearable computing testbed designed to forge stronger links within communities. The testbed exploits Ultra-Wideband (UWB) position sensors, 9-axis motion sensors, edge nodes, and a centralized server, forming a cohesive network that actively facilitates community interactions and engagements. By employing anchors and targets within the UWB sensors, the system achieves high precision in location and distance measurements, laying the groundwork for various proximity-based applications. Integrating 9-axis motion sensors and advanced edge nodes further underscores the system's versatility and robustness in wearable and edge computing. This paper delves into an in-depth exploration and evaluation of the proposed system's architecture, design, and implementation processes. It provides a comprehensive analysis of experimental results and discusses the system's potential impact on enhancing community networks, along with the future directions this technology could take.

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Section: A Proximity-based Systems In Communitiesmentioning

confidence: 99%

Development and Validation of a Proximity-based Wearable Computing Testbed for Community-oriented Wearable Systems

Li,

Mangione,

Porreca

et al. 2024

2024 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

View full text Add to dashboard Cite

show abstract

“…Most studies in LBSN analysis arose from the urban computing field and social media networks analysis mainly due to the availability of large-scale databases such as Foursquare, Twitter, Instagram, and Google Places (Liu et al 2019;Steiger et al 2015;Advaith et al 2020;Rahmani et al 2022). In this body of research, the interdependency between spatial and social contacts lies in the co-presence of two persons in the same physical locations, or the sharing of a similar location history, common behavior, or activities.…”

Section: Location-based Social Network (Lbsn) Analysismentioning

confidence: 99%

A novel metric to measure spatio-temporal proximity: a case study analyzing children’s social network in schoolyards

et al. 2023

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The present study aims to infer individuals’ social networks from their spatio-temporal behavior acquired via wearable sensors. Previously proposed static network metrics (e.g., centrality measures) cannot capture the complex temporal patterns in dynamic settings (e.g., children’s play in a schoolyard). Moreover, existing temporal metrics overlook the spatial context of interactions. This study aims first to introduce a novel metric on social networks in which both temporal and spatial aspects of the network are considered to unravel the spatio-temporal dynamics of human behavior. This metric can be used to understand how individuals utilize space to access their network, and how individuals are accessible by their network. We evaluate the proposed method on real data to show how the proposed metric impacts performance of a clustering task. Second, this metric is used to interpret interactions in a real-world dataset collected from children playing in a playground. Moreover, by considering spatial features, this metric provides unique knowledge of the spatio-temporal accessibility of individuals in a community, and more clearly captures pairwise accessibility compared with existing temporal metrics. Thus, it can facilitate domain scientists interested in understanding social behavior in the spatio-temporal context. Furthermore, We make our collected dataset publicly available for further research.

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Knowledge Discovery and Visualization of UMLS Based on Complex Network Analysis

Jayan,

Harikumar

2023

Lecture Notes in Networks and Systems

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A Proximity Based Community Detection in Temporal Graphs

Cited by 4 publications

References 17 publications

Development and Validation of a Proximity-based Wearable Computing Testbed for Community-oriented Wearable Systems

Development and Validation of a Proximity-based Wearable Computing Testbed for Community-oriented Wearable Systems

A novel metric to measure spatio-temporal proximity: a case study analyzing children’s social network in schoolyards

Knowledge Discovery and Visualization of UMLS Based on Complex Network Analysis

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