Unfairness in Distributed Graph Frameworks

Hao Zhang, Malith Jayaweera, Bin Ren, Yanzhi Wang, Sucheta Soundarajan

Research output: Chapter in Book/Entry/PoemConference contribution


In the era of big data, distributed graph processing frameworks have become important in processing large-scale graph datasets. Such distributed frameworks exhibit major advantages with respect to scalability, and provide various ways to speed up sequential graph algorithms. However, the literature lacks an analysis on the fairness properties of such distributed algorithms. In this work, we analyze several important distributed frameworks and graph analysis algorithms with respect to their fairness properties. Across numerous real-world network datasets, we demonstrate that distributed algorithms often exhibit worse fairness performance as compared to their sequential counterparts. Moreover, we observe that this phenomenon is often strongly connected to the homophily of the graph dataset- the tendency of nodes to connect to other nodes of the same class.

Original languageEnglish (US)
Title of host publicationProceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023
EditorsGuihai Chen, Latifur Khan, Xiaofeng Gao, Meikang Qiu, Witold Pedrycz, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350307887
StatePublished - 2023
Event23rd IEEE International Conference on Data Mining, ICDM 2023 - Shanghai, China
Duration: Dec 1 2023Dec 4 2023

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference23rd IEEE International Conference on Data Mining, ICDM 2023


  • n/a

ASJC Scopus subject areas

  • General Engineering


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