Quantification of network structural dissimilarities based on network embedding
Summary: Quantifying structural dissimilarities between networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path and degree, which only contain part of the topological informati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004222007179 |
| _version_ | 1828318035050496000 |
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| author | Zhipeng Wang Xiu-Xiu Zhan Chuang Liu Zi-Ke Zhang |
| author_facet | Zhipeng Wang Xiu-Xiu Zhan Chuang Liu Zi-Ke Zhang |
| author_sort | Zhipeng Wang |
| collection | DOAJ |
| description | Summary: Quantifying structural dissimilarities between networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path and degree, which only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, which considers the global structural information. In detail, we first construct a distance matrix for each network based on the distances between node embedding vectors derived from DeepWalk. Then, we define the dissimilarity between two networks based on Jensen-Shannon divergence of the distance distributions. Experiments on both synthetic and empirical networks show that our method outperforms the baseline methods and can distinguish networks well. In addition, we show that our method can capture network properties, e.g., average shortest path length and link density. Moreover, the experiment of modularity further implies the functionality of our method. |
| first_indexed | 2024-04-13T17:37:14Z |
| format | Article |
| id | doaj.art-b418d4b50b69420f814561be16f776d1 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-04-13T17:37:14Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-b418d4b50b69420f814561be16f776d12022-12-22T02:37:20ZengElsevieriScience2589-00422022-06-01256104446Quantification of network structural dissimilarities based on network embeddingZhipeng Wang0Xiu-Xiu Zhan1Chuang Liu2Zi-Ke Zhang3Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR ChinaResearch Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China; Corresponding authorResearch Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR ChinaCollege of Media and International Culture, Zhejiang University, Hangzhou 310058, PR China; Corresponding authorSummary: Quantifying structural dissimilarities between networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path and degree, which only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, which considers the global structural information. In detail, we first construct a distance matrix for each network based on the distances between node embedding vectors derived from DeepWalk. Then, we define the dissimilarity between two networks based on Jensen-Shannon divergence of the distance distributions. Experiments on both synthetic and empirical networks show that our method outperforms the baseline methods and can distinguish networks well. In addition, we show that our method can capture network properties, e.g., average shortest path length and link density. Moreover, the experiment of modularity further implies the functionality of our method.http://www.sciencedirect.com/science/article/pii/S2589004222007179Computer scienceNetworkNetwork topology |
| spellingShingle | Zhipeng Wang Xiu-Xiu Zhan Chuang Liu Zi-Ke Zhang Quantification of network structural dissimilarities based on network embedding iScience Computer science Network Network topology |
| title | Quantification of network structural dissimilarities based on network embedding |
| title_full | Quantification of network structural dissimilarities based on network embedding |
| title_fullStr | Quantification of network structural dissimilarities based on network embedding |
| title_full_unstemmed | Quantification of network structural dissimilarities based on network embedding |
| title_short | Quantification of network structural dissimilarities based on network embedding |
| title_sort | quantification of network structural dissimilarities based on network embedding |
| topic | Computer science Network Network topology |
| url | http://www.sciencedirect.com/science/article/pii/S2589004222007179 |
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