Visual evaluation of graph representation learning based on the presentation of community structures

Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization,random walk,and deep learning.However,choosing the right method for different tasks can be challenging.Communities within networks help reveal underlying structures and correlations.Investigating how different models preserve community properties is crucial for identifying the best graph representation for data analysis.This paper defines indicators to explore the perceptual quality of community properties in representation learning spaces,including the consistency of community structure,node distribution within and between communities,and central node distribution.A visualization system presents these indicators,allowing users to evaluate models based on community structures.Case studies demonstrate the effectiveness of the indicators for the visual evaluation of graph representation learning models.

代表政治与代议政治的重叠:以人民代表大会制度为中心的观察

人民代表大会制度是我国的根本政治制度,在制度设计上既吸收了现代政治文明的成果,又继承了中国传统政治文明的核心要素。更为重要的是,人民代表大会制度是中国共产党领导的社会革命的产物,因此它体现和巩固了社会革命的逻辑。代表政治和代议政治的重叠是人民代表大会制度运行过程的核心内容,它在代表政治所维护的人民整体利益的基础上不断推进代议政治所维护的公民利益,同时以代议政治所维护的公民利益的程度来检验和确认代表政治所维护的人民整体利益的真实性。人民代表大会制度运行过程蕴含着自身的政治原理,即在中国共产党的领导、人民整体力量、人民代表大会制度同构的基础上保障现代政治大一统。