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，可以使用大量的社交数据，可以通过社交网络分析来使用它们来建模和研究现实生活中复杂系统（例如社会和经济体系）的结构和动态。它为行业和决策者提供了广泛的机会，可以优化其程序并提高广泛应用领域的效率，从营销和活动分析到以用户为中心的建议系统，以及从动态资源分配到能源管理和城市规划。社交网络主要以图形结构为模型，其中社会参与者由节点和边缘代表，代表它们之间的相互作用或关系。但是，最复杂和社会系统中的社会参与者通常只是多个网络的成员，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联系在一起，而他们也可以在专业网络中连接。在多个社交网络中拥有会员资格只是对他们的行动和决策过程产生了增强的影响，强调了对多层社交网络分析的需求，其中每个层都代表参与者在不同社交环境和环境中的互动和行为（例如，友谊网络）。近年来，对这些社交网络的结构引起了很多关注。但是，由于其复杂而动态的性质，该领域仍然存在许多开放问题。该建议着重于开发有效，有效的解决方案，以探索动态多层社交网络的共同发展，并使用计算智能和社交网络分析技术随着时间的推移研究其行为。我们构建了一个多层双疗法进化框架，该框架利用从网络的结构和个人行为中提取的知识来跟踪共同进化。我们还研究了这些网络中的社会影响力，并定义了测量指标和方法。此外，我们将使用拟议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共同发展的网络中多目标搜索优化的问题，并解决与其异质，大规模和复杂结构相关的关键问题，挑战和机会。