2种社团发现算法对工业共生网络演化过程描述的比较  

The comparison of industrial symbiosis network evolution with two community discovery algorithms

作　　者：王政[1] 王迎春[1] 黄耀国[1] 贾小平[2] 王芳[2] 

机构地区：[1]青岛科技大学化工学院,山东青岛266042 [2]青岛科技大学环境与安全工程学院,山东青岛266042

出　　处：《计算机与应用化学》2015年第4期397-402,共6页Computers and Applied Chemistry

基　　金：国家自然科学基金项目(21136003;41101570);山东省自然科学基金项目(ZR2009BL021)

摘　　要：针对工业共生网络特点,分别应用改进后的谱平分算法和凝聚算法2种典型的社团发现算法对卡伦堡及鲁北工业共生网络进行社团划分,并对其集聚性程度进行定量的比较和评价,从而对卡伦堡及鲁北工业共生网络演化过程做出定量描述,并与其实际工业共生网络的演化发展过程做出对比。结果表明,采用复杂网络社团划分算法可以实现对工业共生网络发展演化过程的定量描述,而改进后的谱平分算法比凝聚算法对工业共生网络演化过程的描述更接近其实际的发展状况。According to the characteristics of industrial symbiosis networks, with using improved spectrum split algorithm and condensation algorithm of two kinds of typical community discovery algorithm, Kalundborg and LuBei industrial symbiosis networks can be divided into communities. In addition, the integration level of Kalundborg and LuBei industrial symbiosis networks was evaluated quantificationaUy. The evolution of Kalundborg and LuBei industrial symbiosis network can be quantitative described and compared to its actual development situation. The results showed that the industrial symbiosis network evolution can be described with community discovery algorithm. The improved spectrum split algorithm was more suitable for the industrial symbiosis network evolution description.

关 键 词：工业共生 谱平分法 凝聚算法 比较 集聚性水平 演化 

分 类 号：X192[环境科学与工程—环境科学]