Coevolution and Many-Objective Search Optimization in Multilayer Social Networks

多层社交网络中的协同进化和多目标搜索优化

• 批准号: RGPIN-2022-04017
• 负责人: MoradianZadeh, Pooya
• 金额: $ 1.82万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750372
• 关键词: Coevolution; Many; Objective; Search; Optimization

Nowadays, a vast amount of social data is available, which can be utilized by social network analysis to model and study the structure and dynamics of real-life complex systems such as social and economic systems. It provides a wide range of opportunities for industries and decision-makers to optimize their procedures and increase efficiency across a broad area of applications, from marketing and campaign analysis to user-centric recommendation systems and from dynamic resource allocation to energy management and urban planning. Social networks are mostly modeled as graph structures where the social actors are represented by the nodes and the edges, representing interactions or relationships between them. However, social actors in the most complex and social systems usually are members of multiple networks simultaneously, and more than one kind of relationship exists between them. For example, a person may be linked to someone in a friendship network while they are also connected in a professional network. Having a membership in multiple social networks simultaneously has an enormous impact on their actions and decision-making process, highlighting the need for multi-layer social network analysis, where each layer represents the interactions and behavior of the actors in different social contexts and environments (e.g.,, friendship network). In recent years, much attention has been paid to the structure of these social networks. However, there are still many open problems in the field due to their complex and dynamic nature. This proposal focuses on developing effective and efficient solutions to explore the co-evolution of dynamic multi-layer social networks and study their behaviors over time using computational intelligence and social network analysis techniques. We build a multi-layer dual-inheritance evolutionary framework that utilizes the extracted knowledge from both the networks' structures and individual behavior to track the co-evolution. We also study social influence in these networks and define metrics and methods to measure it.   Additionally, we will use the proposed framework to study the behavior of real-life social networks and investigate their underlying patterns. We will further study the problem of many-objective search optimization in these co-evolving networks and address critical issues, challenges, and opportunities associated with their heterogeneous, large-scale, and complex structures.

如今，存在大量的社会数据，社会网络分析可以利用这些数据来建模和研究现实生活中复杂系统（例如社会和经济系统）的结构和动态，为行业和经济提供了广泛的机会。决策者在广泛的应用领域优化其程序并提高效率，从营销和活动分析到以用户为中心的推荐系统，从动态资源分配到能源管理和城市规划社交网络大多被建模为图结构。其中社会参与者由节点和边代表，代表交互然而，最复杂的社会系统中的社会参与者通常同时是多个网络的成员，并且它们之间存在不止一种关系，例如，一个人可能与友谊网络中的某人有联系。他们还连接在一个专业网络中，同时拥有多个社交网络的成员资格对其行为和决策过程产生巨大影响，这凸显了对多层社交网络分析的需求，其中每一层都代表了交互和行为。不同社会背景和环境中的参与者（例如，友谊近年来，这些社交网络的结构受到了广泛关注，但由于其复杂性和动态性，该领域仍然存在许多开放性问题。我们利用计算智能和社交网络分析技术，利用从网络结构和个体中提取的知识，构建了一个多层双继承进化框架，研究动态多层社交网络的共同进化，并随着时间的推移研究它们的行为。我们还研究了社会影响。此外，我们将使用所提出的框架来研究现实生活中的社交网络的行为并研究其潜在模式。我们将进一步研究这些网络中的多目标搜索优化问题。 -不断发展的网络并解决与其异构、大规模和复杂结构相关的关键问题、挑战和机遇。