Optimal Dynamic Subset Sampling: Theory and Applications
Authors: 
Lu Yi, Hanzhi Wang, Zhewei WeiAuthors Info & Claims
Lu Yi, Hanzhi Wang, Zhewei WeiAuthors Info & ClaimsPages 3116 - 3127
Abstract
We study the fundamental problem of sampling independent events, called subset sampling. Specifically, consider a set of n distinct events S=x1, …, xn, in which each event xi has an associated probability p(xi). The subset sampling problem aims to sample a subset T ⊆ S, such that every xi is independently included in T with probability p(xi). A naive solution is to flip a coin for each event, which takes O(n) time. However, an ideal solution is a data structure that allows drawing a subset sample in time proportional to the expected output size μ=∑i=1n p(xi), which can be significantly smaller than n in many applications. The subset sampling problem serves as an important building block in many tasks and has been the subject of various research for more than a decade.
However, the majority of existing subset sampling methods are designed for a static setting, where the events in set S or their associated probabilities remain unchanged over time. These algorithms incur either large query time or update time in a dynamic setting despite the ubiquitous time-evolving events with varying probabilities in real life. Therefore, it is a pressing need, but still, an open problem, to design efficient dynamic subset sampling algorithms.
In this paper, we propose ODSS, the first optimal dynamic subset sampling algorithm. The expected query time and update time of ODSS are both optimal, matching the lower bounds of the subset sampling problem. We present a nontrivial theoretical analysis to demonstrate the superiority of ODSS. We also conduct comprehensive experiments to empirically evaluate the performance of ODSS. Moreover, we apply ODSS to a concrete application: Influence Maximization. We empirically show that our ODSS can improve the complexities of existing Influence Maximization algorithms on large real-world evolving social networks.
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Index Terms
- Optimal Dynamic Subset Sampling: Theory and Applications
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August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
- General Chairs:
- Ambuj Singh,
- Yizhou Sun,
- Program Chairs:
- Leman Akoglu,
- Dimitrios Gunopulos,
- Xifeng Yan,
- Ravi Kumar,
- Fatma Ozcan,
- Jieping Ye
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Publication History
Published: 04 August 2023
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- Research-article
Funding Sources
- National Key R&D Program of China
- Beijing Outstanding Young Scientist Program
- Alibaba Group through Alibaba Innovative Research Program
- National Natural Science Foundation of China
- the major key project of PCL
- Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education
- Intelligent Social Governance Interdisciplinary Platform, Major Innovation & Planning Interdisciplinary Platform for the ?Double-First Class? Initiative, Public Policy and Decision-making Research Lab, Public Computing Cloud, Renmin University of China
- Beijing Natural Science Foundation
- Huawei-Renmin University joint program on Information Retrieval
Conference
KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 6 - 10, 2023
CA, Long Beach, USA
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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%
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