This book constitutes the proceedings of the 4th International Conference on Social Informatics, SocInfo 2012, held in Lausanne, Switzerland, in December 2012. The 21 full papers, 18 short papers included in this vol...
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ISBN:
(数字)9783642353864
ISBN:
(纸本)9783642353857
This book constitutes the proceedings of the 4th International Conference on Social Informatics, SocInfo 2012, held in Lausanne, Switzerland, in December 2012.
The 21 full papers, 18 short papers included in this volume were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections named: social choice mechanisms in the e-society,computational models of social phenomena, social simulation, web mining and its social interpretations, algorithms and protocols inspired by human societies, socio-economic systems and applications, trust, privacy, risk and security in social contexts.
This book gathers the proceedings of the Sixth International Conference on Computational science and Technology 2019 (ICCST2019), held in Kota Kinabalu, Malaysia, on 29–30 August 2019. The respective contributions of...
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ISBN:
(数字)9789811500589
ISBN:
(纸本)9789811500572;9789811500602
This book gathers the proceedings of the Sixth International Conference on Computational science and Technology 2019 (ICCST2019), held in Kota Kinabalu, Malaysia, on 29–30 August 2019. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods.
Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods us...
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Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.
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