Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

IoV environment exploring coordination:A federated learning approach

Exploring open fields with coordinated unmanned vehicles is popular in academia and industries.One of the most impressive applicable approaches is the Internet of Vehicles(lov).The IoV connects vehicles,road infrastructures and communication facilities to provide solutions for exploration tasks.However,the coordination of acquiring information from multi-vehicles may risk data privacy.To this end,sharing high-quality experiences instead of raw data has become an urgent demand.This paper employs a Deep Reinforcement Learning(DRL)method to enable IoVs to generate training data with prioritized experience and states,which can support the IoV to explore the environment more efficiently.Moreover,a Federated Learning(FL)experience sharing model is established to guarantee the vehicles'privacy.The numerical results show that the proposed method presents a better successful sharing rate and a more stable convergence within the comparison of fundamental methods.The experiments also suggest that the proposed method could support agents without full information to achieve the tasks.

An Overview of Social Analysis from Mobile Telephone Calls of Different Operators

We construct a call network digraph G with attribution using mobile phone call records within 21 days collected by three operators and analyze the social call behavior features through analyzing the degree distribution of nodes of the network digraph under the help of Graphx based on the Spark Platform.We get the following social conclusions from the communication relationship between phone users:on average,users make about5~7 mobile telephone calls and connect about2~3 persons one day;on weekends,people make less calls but spend longer time on each call,revealing that working calls account for a large part of calls on weekdays;among these telephone calls on weekdays,most are less than one minute,and on average,mobile users that call more people also tend to be called by more individuals.

基于单细胞数据的癌症协同驱动模块识别方法

从大规模生物组学数据中准确识别导致癌症发生的协同驱动模块是生物信息学研究领域重大课题之一.现有研究方法通常只基于批量组学数据进行识别,忽视了细胞水平上的癌症异质性,易受噪声影响.针对上述问题,本文提出了一种基于单细胞数据和先验知识指导的协同驱动模块识别方法CDMFinder.该方法首先利用基因在不同亚型及正常细胞表达数据间存在的特异性共表达信息,融合基因交互网络,优化形成分子功能关联网络,在深入挖掘基因间功能关联的同时有效降低网络复杂度;再基于重叠马尔可夫(Markov)聚类从该网络中挖掘功能簇,并提出基于融合权重和贪心策略的驱动模块识别方法,从功能簇中获得驱动模块集合;最后,融合功能交互网络与突变共现定义模块距离函数,识别获取协同驱动模块. CDMFinder充分融合评估了表达、突变、差异分析等多种因素,展现了优良的识别性能.在乳腺癌和胶质母细胞瘤多组学数据上的实验结果表明,本文方法能够识别出超过对比方法 1.35倍的驱动基因,识别到的协同驱动模块在功能/通路水平富集度上超过现有算法1.5倍.

Heterogeneous Network-Based Chronic Disease Progression Mining

Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. In particular, healthcare insurance fraud in chronic diseases is especially rampant. Understanding disease progression can help investigators detect healthcare insurance frauds early on. Existing disease progression methods often ignore complex relations, such as the time-gap and pattern of disease occurrence. They also do not take into account the different medication stages of the same chronic disease, which is of great help when conducting healthcare insurance fraud detection and reducing healthcare costs. In this paper, we propose a heterogeneous network-based chronic disease progression mining method to improve the current understanding on the progression of chronic diseases, including orphan diseases. The method also considers the different medication stages of the same chronic disease. Extensive experiments show that our method can outperform the existing methods by 20% in terms of F-measure.