Link prediction in complex networks: A survey
Reads0
Chats0
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
More filters
Posted Content
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning
Binxuan Huang,Kathleen M. Carley +1 more
TL;DR: In this paper, a graph neural network class named recurrent graph Neural network (RGNN), that address the shortcomings of prior methods by using recurrent units to capture the long-term dependency across layers, is presented.
Proceedings ArticleDOI
Time Frame based Link Prediction in Directed Citation Networks
TL;DR: The proposed method for analyzing the development of topological measures in a citation network on a specific pried of time finds satisfactory results and is promising.
Journal ArticleDOI
Time series analysis to predict link quality of wireless community networks
TL;DR: This work focuses on link quality prediction by means of a time series analysis and demonstrates that it is possible to accurately predict the link quality in 98% of the instances, both in the short and the long terms.
Journal ArticleDOI
CSTeller: forecasting scientific collaboration sustainability based on extreme gradient boosting
TL;DR: An extreme gradient boosting-based collaboration sustainability prediction model named CSTeller is devised and proposed to analyze the sustainability of scientific collaboration from the perspectives of collaboration duration and collaboration times and investigates factors that may affect collaboration sustainability based on scholars’ local properties and network properties.
Journal ArticleDOI
Weight prediction in complex networks based on neighbor set
TL;DR: This work develops a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node, which can provide accurate predictions of link weights in both cases.
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.