Measuring the impact of influence on individuals: roadmap to quantifying attitude
Measuring the impact of influence on individuals: roadmap to quantifying attitude
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DOI：
10.1109/asonam49781.2020.9381300
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发表时间：
2020-10
影响因子：
2.8
通讯作者：
Xiaoyun Fu;M. Padmanabhan;R. Kumar;Samik Basu;Shawn F. Dorius;A. Pavan
中科院分区：
文献类型：
--
作者：
Xiaoyun Fu;M. Padmanabhan;R. Kumar;Samik Basu;Shawn F. Dorius;A. Pavan
在过去的几十年里，信息在社会网络中的扩散一直是研究的热点，因为它通过群体/个人的影响在塑造公共话语方面产生了重大影响。现有的研究主要将影响建模为实体的二元属性：受影响的或不受影响的。虽然这是一个有用的抽象概念，但它摒弃了影响力程度的概念，即某些人可能比其他人更受影响。我们引入了态度的概念，正如社会心理学所描述的那样，态度是一个实体受到信息影响的程度。态度直观地反映了一个实体的不同邻居对后者产生影响的数量。我们提出了一个信息扩散模型(AIC模型)，它量化了个人在社会网络中的影响程度，即个人的态度。利用该模型，我们对态度最大化问题进行了描述和研究。我们证明了姿态计算函数是单调的、子模的，并且姿态最大化问题是NP难的。我们给出了一个贪心算法，其近似保证为(1-1/e)\Documentclass[12pt]{Minimum}\usepackage{amsath}\usepackage{wa ysym}\usepackage{amsfonts}\usepackage{amsbsy}\usepackage{maThrsfs}\usepackage{upgreek}\setlong{\oddsidemarin}{-69pt}\Begin{Document}$(1-1/e)$\end Document}。在AIC模型的背景下，我们研究了两个问题，目的是研究在客观上获得高态度的个人比最大化整个网络的态度更重要的情景。在第一个问题中，我们引入了可行动态度的概念；直觉上，具有可行动态度的人很可能会根据他们获得的态度采取行动。我们证明了计算可操作姿态的函数不同于计算姿态的函数，它是非子模函数而是近似子模函数。给出了最大化网络中可操作态度的近似算法。在第二个问题中，我们考虑确定网络中态度高于某个阈值的个体的数量。在此背景下，用于计算由种子集诱导的态度高于给定阈值的个体数量的函数既不是子模也不是超模。我们提出了启发式算法来实现问题的解决。我们对算法进行了实验评估，并研究了网络中节点姿态的经验特性，如高姿态节点的空间分布和值分布。
Diffusion of information in social network has been the focus of intense research in the recent past decades due to its significant impact in shaping public discourse through group/individual influence. Existing research primarily models influence as a binary property of entities: influenced or not influenced. While this is a useful abstraction, it discards the notion of degree of influence, i.e., certain individuals may be influenced “more” than others. We introduce the notion of attitude, which, as described in social psychology, is the degree by which an entity is influenced by the information. Intuitively, attitude captures the number of distinct neighbors of an entity influencing the latter. We present an information diffusion model (AIC model) that quantifies the degree of influence, i.e., attitude of individuals, in a social network. With this model, we formulate and study attitude maximization problem. We prove that the function for computing attitude is monotonic and sub-modular, and the attitude maximization problem is NP-Hard. We present a greedy algorithm for maximization with an approximation guarantee of (1-1/e)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1-1/e)$$\end{document}. In the context of AIC model, we study two problems, with the aim to investigate the scenarios where attaining individuals with high attitude is objectively more important than maximizing the attitude of the entire network. In the first problem, we introduce the notion of actionable attitude; intuitively, individuals with actionable attitude are likely to “act” on their attained attitude. We show that the function for computing actionable attitude, unlike that for computing attitude, is non-submodular and however is approximately submodular. We present approximation algorithm for maximizing actionable attitude in a network. In the second problem, we consider identifying the number of individuals in the network with attitude above a certain value, a threshold. In this context, the function for computing the number of individuals with attitude above a given threshold induced by a seed set is neither submodular nor supermodular. We present heuristics for realizing the solution to the problem. We experimentally evaluated our algorithms and studied empirical properties of the attitude of nodes in network such as spatial and value distribution of high attitude nodes.