Quantification of network structural dissimilarities based on network embedding
Zhipeng Wang, Xiu‐Xiu Zhan, Chuang Liu, Zi‐Ke Zhang
Abstract
. 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.
Topics & Concepts
EmbeddingComputer scienceModularity (biology)Average path lengthShortest path problemNode (physics)Network scienceDivergence (linguistics)Distance matrixBiological networkConstruct (python library)Theoretical computer sciencePath (computing)Degree (music)Topology (electrical circuits)Complex networkAlgorithmMathematicsArtificial intelligenceCombinatoricsComputer networkPhysicsGraphBiologyEngineeringPhilosophyStructural engineeringAcousticsWorld Wide WebGeneticsLinguisticsComplex Network Analysis TechniquesBioinformatics and Genomic NetworksAdvanced Graph Neural Networks