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The art of characterization in large networks: Finding the critical attributes

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Abstract

Recently, with the development of online social networks, users in social networks are usually associated with attributes such as user preferences, which is of great importance for analyzing the properties of social networks. To identify critical attributes, we propose and investigate a new problem named attribute k-core maximization. Given an attribute graph G and a budget b, we aim to identify a set of b attributes, such that the corresponding attribute k-core is maximized. Due to the NP-hardness of the problem, we resort to the greedy strategy in this paper. In order to handle large graphs, a layer-based structure and novel searching paradigms are developed to accelerate the computation. Finally, experiments over 6 real-world networks are conducted to evaluate the performance of proposed model and techniques.

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Notes

  1. http://dblp.uni-trier.de/xml

  2. https://www.flickr.com

  3. https://www.yelp.com/dataset

  4. https://snap.stanford.edu/data

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Acknowledgments

This work is support by NSFC 61802345, ZJNSF LQ20F020007, ZJNSF LY21F020012 and Y202045024.

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Authors and Affiliations

  1. Zhejiang Gongshang University, Hangzhou, China

    Renjie Sun, Chen Chen, Xiaoyang Wang, Yanping Wu, Mengqi Zhang & Xijuan Liu

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  1. Renjie Sun
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  2. Chen Chen
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  3. Xiaoyang Wang
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  4. Yanping Wu
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  5. Mengqi Zhang
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  6. Xijuan Liu
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Correspondence to Chen Chen.

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This article belongs to the Topical Collection: Special Issue on Large Scale Graph Data Analytics

Guest Editors: Xuemin Lin, Lu Qin, Wenjie Zhang, and Ying Zhang

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Sun, R., Chen, C., Wang, X. et al. The art of characterization in large networks: Finding the critical attributes. World Wide Web 25, 655–677 (2022). https://doi.org/10.1007/s11280-021-00904-4

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