scholarly journals Structural Patterns in Complex Networks through Spectral Analysis

2010 ◽  
pp. 45-59 ◽  
Author(s):  
Ernesto Estrada
Keyword(s):  
Spectral Analysis ◽  
Complex Networks ◽  
Structural Patterns

Synchronized regions of pinned complex networks: spectral analysis

2014 ◽  
Vol 78 (3) ◽  
pp. 1609-1628 ◽  
Author(s):  
Linying Xiang ◽  
Fei Chen ◽  
Guanrong Chen
Keyword(s):  
Spectral Analysis ◽  
Complex Networks

scholarly journals Spectral analysis and the dynamic response of complex networks

2005 ◽  
Vol 71 (1) ◽  
Author(s):  
M. A. M. de Aguiar ◽  
Y. Bar-Yam
Keyword(s):  
Spectral Analysis ◽  
Dynamic Response ◽  
Complex Networks

Detecting Multi-Scale Community Structure in Complex Networks

2015 ◽  
Vol 740 ◽  
pp. 881-884
Author(s):  
Yu Quan Guo ◽  
Xiong Fei Li
Keyword(s):  
Spectral Analysis ◽  
Community Structure ◽  
Complex Networks ◽  
Multiple Scale ◽  
Label Propagation ◽  
Dynamic Efficiency ◽  
Heuristic Function ◽  
Multi Scale ◽  
Propagation Algorithm ◽  
Novel Algorithm

Multiple-scale community of complex networks has attracted much attention. For the problem, previous methods can not investigate multiple-scale property of community. To address this, we propose a novel algorithm (h_LPA) to detect multiple-scale structure of community. The algorithm is a heuristic label propagation algorithm associated with spectral analysis of complex networks. Label updating strategy of h_LPA is combined with heuristic function from the perspective of networks dynamics. The heuristic function further improves the dynamic efficiency of h_LPA. Extensive tests on artificial networks and real world networks give excellent results.

scholarly journals Link Prediction through Deep Generative Model

2018 ◽  
Author(s):  
Xu-Wen Wang ◽  
Yize Chen ◽  
Yang-Yu Liu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Prediction Method ◽  
Generative Models ◽  
Generative Model ◽  
Superior Performance ◽  
Prediction Problem ◽  
Structural Patterns ◽  
Domain Specific

AbstractInferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine1–3, e-commerce4, social media5 and criminal intelligence6. Numerous methods have been proposed to solve the link prediction problem7–9. Yet, many of these existing methods are designed for undirected networks only. Moreover, most methods are based on domain-specific heuristics10, and hence their performances differ greatly for networks from different domains. Here we developed a new link prediction method based on deep generative models11 in machine learning. This method does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities12. Conceptually, taking into account structural patterns at different scales all together should outperform any domain-specific heuristics that typically focus on structural patterns at a particular scale. Indeed, when applied to various real-world networks from different domains13–17, our method shows overall superior performance against existing methods. Moreover, it can be easily parallelized by splitting a large network into several small subnetworks and then perform link prediction for each subnetwork in parallel. Our results imply that deep learning techniques can be effectively applied to complex networks and solve the classical link prediction problem with robust and superior performance.SummaryWe propose a new link prediction method based on deep generative models.

Sign in / Sign up

Export Citation Format

Share Document