Link prediction in complex networks: A survey
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TLDR
Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.Abstract:
Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.read more
Citations
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Journal ArticleDOI
Tag-aware link prediction algorithm in complex networks
TL;DR: This paper claims that a node’s tags are insufficient to profile this node, and the tags of this node's neighbors are also considerable, and proposes a link prediction algorithm by harnessing the homogeneity of the tag system and get more accurate predictions.
Proceedings ArticleDOI
Dimensionality Reduction for Supervised Learning in Link Prediction Problems
Antonio Pecli,Bruno Giovanini,Carla C. Pacheco,Carlos H. A. Moreira,Fernando Ferreira,Frederico Tosta,Júlio Tesolin,Marcio Vinicius Dias,Silas P. Lima Filho,Maria Cláudia Cavalcanti,Ronaldo R. Goldschmidt +10 more
TL;DR: The results of experiments show that dimensionality reduction with PCA and FFS can improve model precision in this kind of problem and are evaluated as a preprocessing stage to the binary classifier construction in link prediction applications.
Proceedings ArticleDOI
Community-Adaptive Link Prediction
Hyoungjun Jeon,Taewhan Kim +1 more
TL;DR: An adaptive approach is proposed, in which two separate link predictions depending on inter or intra links in community are used, and then balance the links based on the degree of community influence on link prediction.
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Inferring complex networks from time series of dynamical systems: Pitfalls, misinterpretations, and possible solutions
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A Nodes' Evolution Diversity Inspired Method to Detect Anomalies in Dynamic Social Networks
Huan Wang,Chunming Qiao +1 more
TL;DR: A quantum mechanism based particle swarm optimization algorithm (QMPSO) in NEDM determines the optimal observation states of the behaviors of different nodes, and maximally reflects the evolutional fluctuations in the evolution processes of social networks.
References
More filters
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.