CRII: Interpretable Influence Propagating and Blocking on Graphs

CRII：图上可解释的影响传播和阻塞

• 批准号: 2153369
• 负责人: Zhiqian Chen
• 金额: $ 17.4万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153369&HistoricalAwards=false
• 关键词: CRII; Interpretable; Influence; Propagating; Blocking

As networks including virtual (e.g., social networks) and physically grounded (e.g., transportation networks) increase in complexity, the need to understanding the spread of network influence is crucial. Influence in networks has been the subject of increasing attention among researchers due to its far-reaching social and commercial implications. For instance, the objective of information propagation or viral marketing is to identify the most prominent trend setters capable of influencing vast numbers of others, while the primary objective of epidemiology is to ascertain who is most likely to spread a disease, which aids in the development of vaccine and quarantine regulations. This project will develop novel tools to analyze how the spreading network's structure and initial state maximize the influence flow, and then investigate policy options for controlling the flows. The primary innovation of this project will be its ability to learn the complex relationship between flows and the geometric structure of graphs and extract understandable rules for decision-makers. The main challenge is the huge number of combinations of variables combined across structures and attributes to alter influence flows. This project advocates a unique paradigm for learning interpretable representations of influence flow over graphs, with a particular emphasis on disentangling the combinatorial limitations imposed by both the graph's geometric structure and seed selection. This research aims at developing a new framework that boosts accuracy and interpretability while decoupling the influence process. The difficulty of developing interpretable models is compounded by the specific characteristics of influence modeling, which include complex tangled topological links, insufficient data, and confluence effects. The investigator will use context-aware constraints and complementing observations to narrow the search and determine the effect source. The investigator will perform an in-depth assessment of effective and efficient control policies to improve influence propagation or blocking. This project will address the following three fundamental research issues: learning expository topological dependence of influence; Learning the influence cascade and the sources; and learning to control the influence flows. The proposed model will determine the optimal seeds that minimize future influence based on the currently influenced region, which requires modeling the interaction of numerous graph-dependent elements. According to thermodynamics' second law, the flow rate is determined by the energy levels within the system, which motivates us to examine graph entropy notions further to provide a more robust assessment of them. The investigator will employ global sensitivity analysis and perturbation matrix theory to choose the smallest yet most critical and robust set.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.

作为包括虚拟（例如，社交网络）和物理接地（例如， 随着交通运输网络的复杂性增加，了解网络影响力的传播至关重要。网络中的影响力因其深远的社会和商业影响而日益受到研究者的关注。例如，信息传播或病毒式营销的目标是确定能够影响大量其他人的最突出的趋势制定者，而流行病学的主要目标是确定谁最有可能传播疾病，这有助于制定疫苗和检疫条例。本项目将开发新的工具来分析传播网络的结构和初始状态如何使影响流最大化，然后研究控制影响流的政策选择。该项目的主要创新将是它能够学习流之间的复杂关系和图形的几何结构，并为决策者提取可理解的规则。主要的挑战是大量跨结构和属性的变量组合，以改变影响力流。该项目倡导一种独特的范式，用于学习图上影响流的可解释表示，特别强调解开图的几何结构和种子选择所施加的组合限制。本研究旨在开发一个新的框架，提高准确性和可解释性，同时解耦的影响过程。开发可解释的模型的难度由于影响建模的具体特征而变得更加复杂，这些特征包括复杂的拓扑链接、数据不足和汇流效应。研究者将使用情境感知约束和补充观察来缩小搜索范围并确定影响源。调查员将对有效和高效的控制政策进行深入评估，以改善影响传播或阻止。本计画将探讨以下三个基本研究议题：学习影响力的暂时性拓扑相依性;学习影响力级联与影响力来源;以及学习控制影响力流。所提出的模型将确定最佳的种子，最大限度地减少未来的影响的基础上，目前受影响的地区，这需要建模的许多图形相关的元素的相互作用。根据热力学第二定律，流量由系统内的能级决定，这促使我们进一步研究图熵概念，以提供更可靠的评估。研究人员将采用全球敏感性分析和扰动矩阵理论，以选择最小但最关键和鲁棒的set.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Understanding Influence Maximization via Higher-Order Decomposition

• DOI: 10.1137/1.9781611977653.ch86
• 发表时间: 2022-07
• 作者: Zonghan Zhang;Zhiqian Chen

Memetic Algorithms for Spatial Partitioning Problems

空间分区问题的模因算法

• DOI: 10.1145/3544779
• 发表时间: 2023
• 期刊: ACM Transactions on Spatial Algorithms and Systems
• 影响因子: 1.9
• 作者: Biswas, Subhodip;Chen, Fanglan;Chen, Zhiqian;Lu, Chang-Tien;Ramakrishnan, Naren
• 通讯作者: Ramakrishnan, Naren

Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks

• DOI: 10.1145/3627816
• 发表时间: 2021-07
• 期刊: ACM Computing Surveys
• 影响因子: 16.6
• 作者: Zhiqian Chen;Fanglan Chen;Lei Zhang;Taoran Ji;Kaiqun Fu;Liang Zhao;Feng Chen;Lingfei Wu

Early Forecasting of the Impact of Traffic Accidents Using a Single Shot Observation

使用单次观测早期预测交通事故的影响

• 发表时间: 2022
• 期刊: Proceedings of the 2022 SIAM International Conference on Data Mining (SDM
• 作者: Meng, Guangyu;Jiang, Qisheng;Fu, Kaiqun;Lin, Beiyu;Lu, Chang-Tien;Chen Zhiqian
• 通讯作者: Chen Zhiqian

Blocking Influence at Collective Level with Hard Constraints (Student Abstract)

• DOI: 10.1609/aaai.v36i11.21694
• 发表时间: 2022-06
• 作者: Zonghan Zhang;Subhodip Biswas;Fanglan Chen;Kaiqun Fu;Taoran Ji;Chang-Tien Lu;Naren Ramakrishnan;Zhiqian Chen