Finding Top-k Important Edges on Bipartite Graphs: Ego-betweenness Centrality-based Approaches

Weng, Tongfeng; Zhou, Xu; Fang, Yixiang; Tan, Kian-Lee; Li, Kenli

doi:10.1109/icde55515.2023.00186

2023 IEEE 39th International Conference on Data Engineering (ICDE) 2023

DOI: 10.1109/icde55515.2023.00186

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Finding Top-k Important Edges on Bipartite Graphs: Ego-betweenness Centrality-based Approaches

Tongfeng Weng

Xu Zhou

Yixiang Fang

et al.

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2024

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Cited by 2 publications

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Parallel Contraction Hierarchies Construction on Road Networks

Chen,

Ji,

Yuan

et al. 2024

IEEE Trans. Knowl. Data Eng.

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Parallel Contraction Hierarchies Construction on Road Networks

Chen,

Ji,

Yuan

et al. 2024

IEEE Trans. Knowl. Data Eng.

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DecentralDC: Assessing data contribution under decentralized sharing and exchange blockchain

Ke,

Liu,

Wang

et al. 2024

PLoS ONE

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The issue of data quality has emerged as a critical concern, as low-quality data can impede data sharing, diminish intrinsic value, and result in economic losses. Current research on data quality assessment primarily focuses on four dimensions: intrinsic, contextual, presentational, and accessibility quality, with intrinsic and presentational quality mainly centered on data content, and contextual quality reflecting data usage scenarios. However, existing approaches lack consideration for the behavior of data within specific application scenarios, which encompasses the degree of participation and support of data within a given scenario, offering valuable insights for optimizing resource deployment and business processes. In response, this paper proposes a data contribution assessment method based on maximal sequential patterns of behavior paradigms (DecentralDC). DecentralDC is composed of three steps: (1) mining the maximal sequential patterns of sharing and exchange behavior paradigms; (2) determining the weights of these paradigms; (3) calculating the contribution of sharing and exchange databases combined with data volume. To validate our approach, two sharing and exchange scenarios of different scales are established. The experimental results in two scenarios validate the effectiveness of our method and demonstrate a significant reduction in cumulative regret and regret rate in data pricing due to the introduction of data contribution. Specifically, compared to the most competitive baseline, the improvements of mean average precision in two scenarios are 6% and 8%. The code and simulation scenarios have been open-sourced and are available at https://github.com/seukgcode/DecentralDC.

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Finding Top-k Important Edges on Bipartite Graphs: Ego-betweenness Centrality-based Approaches

Cited by 2 publications

References 58 publications

Parallel Contraction Hierarchies Construction on Road Networks

Parallel Contraction Hierarchies Construction on Road Networks

DecentralDC: Assessing data contribution under decentralized sharing and exchange blockchain

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