Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing efforts have proposed proxy models (transformations) with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations based on network prior knowledge induce different search behaviors with similar characteristics from various perspectives. For a specific case, it is difficult for users to determine a suitable transformation a priori. In this paper, we propose a multi-transformation evolutionary framework for influence maximization (MTEFIM) with convergence guarantees to exploit the potential similarities and unique advantages of alternate transformations and avoid users manually determining the most suitable one. In MTEFIM, multiple transformations are optimized simultaneously as multiple tasks. Each transformation is assigned an evolutionary solver. Three major components of MTEFIM are conducted: 1) estimating the potential relationship across transformations based on the degree of overlap across individuals (seed sets) of different populations, 2) transferring individuals across populations adaptively according to the inter-transformation relationship, 3) selecting the final output seed set containing all the proxy model knowledge. The effectiveness of MTEFIM is validated on both benchmarks and real-world social networks. Experimental results show that MTEFIM can efficiently utilize the potentially transferable knowledge across multiple transformations to achieve highly competitive performance compared to several popular IM-specific methods. The implementation of MTEFIM can be accessed at https://github.com/xiaofangxd/MTEFIM.
翻译:影响最大化是挖掘社会网络深层信息的一个关键问题,社会网络旨在从网络中选择一组种子,以最大限度地增加受影响节点的数量。为了有效评估种子集的影响扩散,现有努力提出了代用模型(变换),降低计算成本,以取代昂贵的蒙特卡洛模拟过程。基于网络的前知识的这些替代变换导致不同搜索行为,具有不同角度的相似特征。具体案例是,用户难以确定一个先验的适当变换。在本文件中,我们提议了一个从网络中挑选一组种子的多变换进化框架,以发挥其他变换的潜在相似性和独特优势,并避免用户手动确定最合适的变换。在MTEFIM中,多种变换是同时优化的,以取代昂贵的蒙特卡洛模拟模拟过程。每个变换过程的三个主要组成部分是:1)根据不同人群个人(种子组合)的重叠程度,估计各种变换的潜在关系;2)根据内部变换关系,将个人从适应性移到异性关系,3)选择其他变换变换的异性优势和独特的变现方法。在IM中,选择最后的变现的变现工具,将所有变现工具,将所有变现为可变现工具。