Removing spurious interactions in complex networks

Abstract

Identifying and removing spurious links in complex networks is a meaningful problem for many real applications and is crucial for improving the reliability of network data, which in turn can lead to a better understanding of the highly interconnected nature of various social, biological and communication systems. In this work we study the features of different simple spurious link elimination methods, revealing that they may lead to the distortion of networks' structural and dynamical properties. Accordingly, we propose a hybrid method which combines similarity-based index and edge-betweenness centrality. We show that our method can effectively eliminate the spurious interactions while leaving the network connected and preserving the network's functionalities.

7 pages, 7 figures

References in corpus (8)

• Finding community structure in networks using the eigenvectors of matrices
• Synchronization in complex networks
• Hierarchical structure and the prediction of missing links in networks
• Predicting Missing Links via Local Information
• Missing and spurious interactions and the reconstruction of complex networks
• Effective and Efficient Similarity Index for Link Prediction of Complex Networks
• Scale-free trees: the skeletons of complex networks
• Role-similarity based functional prediction in networked systems: Application to the yeast proteome

Cited by in corpus (17)

• Enhancing network robustness for malicious attacks
• Link Prediction in Complex Networks: A Mutual Information Perspective
• Resolving structural variability in network models and the brain
• Toward Stronger Robustness of Network Controllability: A Snapback Network Model
• Ranking users, papers and authors in online scientific communities
• Evaluating user reputation in online rating systems via an iterative group-based ranking method
• Group-based ranking method for online rating systems with spamming attacks
• Reconstructing networks
• Improving personalized link prediction by hybrid diffusion
• Convex skeletons of complex networks
• A perturbation-based approach to identifying potentially superfluous network constituents
• DecLiNe -- Models for Decay of Links in Networks
• Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery
• Membership in social networks and the application in information filtering
• Predicting missing links via correlation between nodes
• Reconstructing propagation networks with temporal similarity metrics
• Hidden space reconstruction inspires link prediction in complex networks