III: Small: Collaborative Research: Cost-Efficient Sampling and Estimation from Large-Scale Networks

III：小型：协作研究：大规模网络的经济高效采样和估计

• 批准号: 2209921
• 负责人: Chul-Ho Lee
• 金额: $ 25万
• 依托单位: Texas State University - San Marcos
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209921&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Cost

Sampling and estimating structural information from large-scale networks or graphs has been central to our understanding of the network dynamics and its rich set of applications. Markov Chain Monte Carlo (MCMC) has been the key enabler for a broader context of graph sampling, including estimating the properties of large graphs, sampling the corpus of documents indexed by search engines, sampling records from hidden databases behind Web forms, identifying subgraphs of certain characteristics and frequent graph pattern matching. Despite versatile applications of the MCMC methods and their customized algorithms for analyzing graph-structured data in various forms, there still exist critical challenges and limitations in the literature centered around the MCMC methods. One is the 'cost' consumption/constraints associated with the sampling operation, which limits the size of total samples obtained and negatively affects the accuracy of any estimator based on the obtained samples. Another limitation is that the recent advances in MCMC, especially built up on favorable non-reversible Markov chains, cannot be leveraged to the various large-graph sampling tasks, due to their required global knowledge of the underlying state space, lack of distribution implementation, unconstrained state space, as well as the simplified cost assumption. The goal of this research is to fully exploit the potentials of a set of crawling samplers by making the samplers adaptive and possibly interactive on a properly constructed graph domain, to transcend the current status-quo in the wide range of graph sampling tasks. Specifically, the project aims to: (i) build a theoretical framework to construct a suite of cost-efficient sampling policies by optimally balancing the tradeoff between the sample quality and quantity under challenged access environments with a given cost budget, (ii) design a class of adaptive random walks by fully exploiting the past information to achieve minimal temporal correlations over the obtained samples and by controlling the random walks collectively to enable maximal space exploration, and (iii) extend the standard MCMC toolkits toward faster and more cost-efficient exploration of feasible subgraphs/configurations and computing/optimization on a graph, along with extensive validations to create practical and usable solutions in reality. This research has a high potential impact on a vast range of multi-disciplinary applications, including sampling large-scale graphs for statistical inference and efficient estimation and randomized algorithms for combinatorial optimizations in various disciplines, where the standard MCMC methods have been dominant but also constrained our understanding.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

从大规模网络或图形中采样和估计结构信息一直是我们理解网络动态及其丰富应用的核心。马尔可夫链蒙特卡罗 (MCMC) 一直是更广泛的图采样的关键推动者，包括估计大型图的属性、对搜索引擎索引的文档语料库进行采样、从 Web 表单背后的隐藏数据库中采样记录、识别某些特征的子图以及频繁的图模式匹配。尽管 MCMC 方法及其用于分析各种形式的图结构数据的定制算法具有多种应用，但围绕 MCMC 方法的文献仍然存在关键的挑战和局限性。一是与采样操作相关的“成本”消耗/约束，它限制了获得的总样本的大小，并对基于获得的样本的任何估计器的准确性产生负面影响。另一个限制是，MCMC 的最新进展，尤其是建立在有利的不可逆马尔可夫链上的进展，无法用于各种大图采样任务，因为它们需要底层状态空间的全局知识、缺乏分布实现、不受约束的状态空间以及简化的成本假设。这项研究的目标是通过使采样器在正确构建的图域上具有自适应性和可能的​​交互性，充分发挥一组爬行采样器的潜力，以超越当前广泛的图采样任务的现状。具体来说，该项目的目标是：（i）建立一个理论框架，通过在给定成本预算的挑战访问环境下优化平衡样本质量和数量之间的权衡，构建一套具有成本效益的采样策略，（ii）通过充分利用过去的信息来设计一类自适应随机游走，以实现所获得样本的最小时间相关性，并通过集体控制随机游走来实现最大空间 (iii) 扩展标准 MCMC 工具包，以更快、更经济高效地探索可行的子图/配置和图上的计算/优化，并进行广泛的验证，以在现实中创建实用且可用的解决方案。这项研究对广泛的多学科应用具有很高的潜在影响，包括用于统计推断和有效估计的大规模图形采样以及用于各个学科组合优化的随机算法，其中标准 MCMC 方法一直占主导地位，但也限制了我们的理解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和 更广泛的影响审查标准。

项目成果

Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass

• DOI: 10.1109/iiswc55918.2022.00023
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Symposium on Workload Characterization (IISWC)
• 作者: Xin Huang;Jongryool Kim;Brad Rees;Chul-Ho Lee

GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals

• DOI: 10.1109/icdcs54860.2022.00105
• 发表时间: 2022-07
• 期刊: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Weipeng Zhuo;Ziqi Zhao;Ka Ho Chiu;Shiju Li;Sangtae Ha;Chul-Ho Lee;S. G. Gary Chan

FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals

• DOI: 10.1109/icdcs57875.2023.00039
• 发表时间: 2023-07
• 期刊: 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Weipeng Zhuo;Kaili Chiu;Jierun Chen;Ziqi Zhao;Shueng-Han Gary Chan;Sangtae Ha;Chul-Ho Lee

Controlling Epidemic Spread Under Immunization Delay Constraints

在免疫延迟限制下控制疫情蔓延

• 发表时间: 2023
• 期刊: IFIP Networking 2023
• 作者: Li, Shiju;Huang, Xin;Lee, Chul-Ho Lee;Eun, Do Young
• 通讯作者: Eun, Do Young

Semi-supervised Learning with Network Embedding on Ambient RF Signals for Geofencing Services

• DOI: 10.1109/icde55515.2023.00208
• 发表时间: 2022-10
• 期刊: 2023 IEEE 39th International Conference on Data Engineering (ICDE)
• 作者: Weipeng Zhuo;Kaili Chiu;Jierun Chen;Jiajie Tan;Edmund Sumpena;Shueng-Han Gary Chan;Sangtae Ha