skip to main content
10.1145/3159652.3170460acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
abstract

Connectivity in Complex Networks: Measures, Inference and Optimization

Published: 02 February 2018 Publication History

Abstract

Networks are ubiquitous in many high impact domains. Among the various aspects of network studies, connectivity is the one that plays important role in many applications (e.g., information dissemination, robustness analysis, community detection, etc.). The diversified applications have spurred numerous connectivity measures. Accordingly, ad-hoc connectivity optimization methods are designed for each measure, making it hard to model and control the connectivity of the network in a uniformed framework. On the other hand, it is often impossible to maintain an accurate structure of the network due to network dynamics and noise in real applications, which would affect the accuracy of connectivity measures and the effectiveness of corresponding connectivity optimization methods. In this work, we aim to address the challenges on network connectivity by (1)unifying a wide range of classic network connectivity measures into one uniform model; (2)proposing effective approaches to infer connectivity measures and network structures from dynamic and incomplete input data, and (3) providing a general framework to optimize the connectivity measures in the network.

References

[1]
Sergey V Buldyrev, Roni Parshani, Gerald Paul, H Eugene Stanley, and Shlomo Havlin. 2010. Catastrophic cascade of failures in interdependent networks. Nature 464, 7291 (2010), 1025--1028.
[2]
Deepayan Chakrabarti, Yang Wang, Chenxi Wang, Jurij Leskovec, and Christos Faloutsos. 2008. Epidemic thresholds in real networks. ACM Transactions on Information and System Security (TISSEC) 10, 4 (2008), 1.
[3]
Chen Chen, Jingrui He, Nadya Bliss, and Hanghang Tong. 2015. On the Connectivity of Multi-layered Networks: Models, Measures and Optimal Control. In Data Mining (ICDM), 2015 IEEE 15th International Conference on. IEEE, 715--720.
[4]
Chen Chen, Jingrui He, Nadya Bliss, and Hanghang Tong. 2017. Towards Optimal Connectivity on Multi-Layered Networks. IEEE Transactions on Knowledge and Data Engineering 29, 10 (2017), 2332--2346.
[5]
Chen Chen and Hanghang Tong. 2016. On the eigen-functions of dynamic graphs: Fast tracking and attribution algorithms. Statistical Analysis and Data Mining: The ASA Data Science Journal (2016), 121--135.
[6]
Chen Chen, Hanghang Tong, Lei Xie, Lei Ying, and Qing He. 2016. FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13--17, 2016. 765--774.
[7]
Wei Chen, Wynne Hsu, and Mong Li Lee. 2013. Making recommendations from multiple domains. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 892--900.
[8]
Jianxi Gao, Sergey V Buldyrev, H Eugene Stanley, and Shlomo Havlin. 2012. Networks formed from interdependent networks. Nature physics 8, 1 (2012), 40--48.
[9]
Frank Harary and Allen Schwenk. 1979. The spectral approach to determining the number of walks in a graph. Pacific J. Math. 80, 2 (1979), 443--449.
[10]
Shlomo Hoory, Nathan Linial, and Avi Wigderson. 2006. Expander graphs and their applications. Bull. Amer. Math. Soc. 43, 4 (2006), 439--561.
[11]
WU Jun, Mauricio Barahona, Tan Yue-Jin, and Deng Hong-Zhong. 2010. Natural connectivity of complex networks. Chinese Physics Letters 27, 7 (2010), 078902.
[12]
Bin Li, Qiang Yang, and Xiangyang Xue. 2009. Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction. In IJCAI, Vol. 9. 2052--2057.
[13]
Zhongqi Lu, Weike Pan, Evan Wei Xiang, Qiang Yang, Lili Zhao, and ErHeng Zhong. 2013. Selective transfer learning for cross domain recommendation. In SDM. SIAM, 641--649.
[14]
Mark EJ Newman. 2008. The mathematics of networks. The new palgrave encyclopedia of economics 2, 2008 (2008), 1--12.
[15]
Roni Parshani, Sergey V Buldyrev, and Shlomo Havlin. 2010. Interdependent networks: Reducing the coupling strength leads to a change from a first to second order percolation transition. Physical review letters 105, 4 (2010), 048701.
[16]
Arunabha Sen, Anisha Mazumder, Joydeep Banerjee, Arun Das, and Randy Compton. 2014. Multi-layered Network Using a New Model of Interdependency. arXiv preprint arXiv:1401.1783 (2014).
[17]
Charalampos E Tsourakakis. 2008. Fast counting of triangles in large real networks without counting: Algorithms and laws. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. IEEE, 608--617.
[18]
Deqing Yang, Jingrui He, Huazheng Qin, Yanghua Xiao, and Wei Wang. 2015. A Graph-based Recommendation across Heterogeneous Domains. In Proceedings of the 24rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 463--472.

Cited By

View all
  • (2024)Improving Service Quality: Innovations in Enriching the IoT Experience2024 7th International Conference on Electronics, Communications, and Control Engineering (ICECC)10.1109/ICECC63398.2024.00019(66-71)Online publication date: 22-Mar-2024
  • (2023)Measures and Optimization for Robustness and Vulnerability in Disconnected NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.327997918(3350-3362)Online publication date: 2023

Index Terms

  1. Connectivity in Complex Networks: Measures, Inference and Optimization

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
    February 2018
    821 pages
    ISBN:9781450355810
    DOI:10.1145/3159652
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 02 February 2018

    Check for updates

    Author Tags

    1. graph mining
    2. network connectivity

    Qualifiers

    • Abstract

    Conference

    WSDM 2018

    Acceptance Rates

    WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;
    Overall Acceptance Rate 498 of 2,863 submissions, 17%

    Upcoming Conference

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)7
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Improving Service Quality: Innovations in Enriching the IoT Experience2024 7th International Conference on Electronics, Communications, and Control Engineering (ICECC)10.1109/ICECC63398.2024.00019(66-71)Online publication date: 22-Mar-2024
    • (2023)Measures and Optimization for Robustness and Vulnerability in Disconnected NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.327997918(3350-3362)Online publication date: 2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media