Identifying Key Resources in a Social Network Using f-PageRank

Kamal, Imam Mustafa; Bae, Hyerim; Liu, Ling; Choi, Yongki

doi:10.1109/icws.2017.45

Cited by 8 publications

(2 citation statements)

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“…We utilize the power iteration method to obtain the spreader rank of each node. To guarantee convergence of the power iteration, matrix C should satisfy stochastic, aperiodic, and irreducibility conditions [40], [41]. Stochastic means that the matrix column sum must be 1.…”

Section: E Spreader Centralitymentioning

confidence: 99%

Trajectory Linkage and Spreader Centrality for Social Epidemic Networks

2020

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Constructing social networks for real epidemic cases is very challenging. Many mathematical models have been proposed to model such networks using simulation models, such as susceptibleinfected (SI), susceptible-infected-recovered (SIR), and susceptible-exposed-infectious-removed (SEIR). Nonetheless, social network analyses can fail to capture real conditions, as such models are constructed based on many assumptions. Furthermore, unlike standard online social networks (OSNs), a social epidemic network requires different treatment from both model construction and social network analysis perspectives, especially for the detection of superspreaders. To address these issues, we propose a trajectory linkage method to automatically discover social networks from historical patient-trajectory data, wherein relations among patients are determined by spatial proximity and time-windows. Moreover, we introduce a novel spreader centrality measure that is devised to identify superspreaders in a social epidemic network. Extensive experiments were performed using real epidemic data. The results revealed that trajectory linkage can obtain a denser social network model than is possible by only incorporating patient data (the "who is infected by whom" relationship). By performing a social network analysis, the trajectory linkage model can express the real conditions of the patient relationship. Furthermore, our spreader centrality can capture the real superspreaders more effectively than can the existing centrality measure in social epidemic networks. INDEX TERMS Social networks, epidemic networks, network centrality, spreader centrality, social network analysis.

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Section: E Spreader Centralitymentioning

confidence: 99%

Trajectory Linkage and Spreader Centrality for Social Epidemic Networks

2020

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“…Most representatively, PageRank can be used for web search using large web-scale graphs [4], [5]. Social networks are used to analyze trends by extracting the key resources or calculating a ranking of users [6], [7], [8]. PageRank in e-commerce system allows users to get the appropriate recommendation of products [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-Mode SpMV Accelerator for Transprecision PageRank With Real-World Graphs

Kim

Lee

et al. 2023

IEEE Access

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With the development of Internet networks, the PageRank algorithm, which was initially developed to recommend important pages in Google's web search systems, is widely used as the basis of various ranking systems in graph processing fields. However, PageRank algorithm requires Sparse Matrix-Vector Multiplication (SpMV) repeatedly which becomes main bottleneck for the calculation. In this study, we present the multi-mode SpMV accelerator for half-to-single transprecision PageRank with real-world graphs. To support transprecision, where the operation performs in half-precision (FP16) initially and changes its precision to single-precision (FP32), the proposed multi-mode SpMV accelerator can perform both dual FP16 mode and single FP32 mode. In dual FP16 mode, the proposed accelerator performs two FP16 SpMV in parallel, and in single FP32 mode, it performs one FP32 SpMV with the same hardware resources. Also, for the reduction of memory footprint, the proposed accelerator supports the Compressed Sparse Row (CSR) format. In addition, dual-issue accumulator and multi-mode transprecision multiplier are presented to support both FP16 and FP32 modes. Validation of the proposed transprecision PageRank algorithm is performed with four real-world graph datasets, resulting in a low 0-4% error rate and 1.3×-1.9× speedup compared to single-precision PageRank computation without transprecision.

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