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
Journal ArticleDOI
Inferring Functional Connectivity From Time-Series of Events in Large Scale Network Deployments
TL;DR: A novel inference approach whereby two nodes are defined as forming a functional edge if they emit substantially more coincident or short-lagged events than would be expected if they were statistically independent.
Entity Extraction and Consolidation for Social Web Content Preservation
Stefan Dietze,Diana Maynard,Elena Demidova,Thomas Risse,Wim Peters,Katerina Doka,Yannis Stavrakas +6 more
TL;DR: This paper presents an approach which is based on an iterative cycle exploiting Web for entity extraction, detection, and entity correlation, and the long-term goal is to preserve Web over time and allow its navigation and analysis based on well-formed RDF data about entities.
Proceedings ArticleDOI
A Community Bridge Boosting Social Network Link Prediction Model
TL;DR: This work proposes a Community Bridge Boosting Prediction Model (CBBPM) that treats certain bridge nodes differently depending on their structural position and shows that such bridge node similarity boosting mechanism can improve the accuracy of traditional link prediction methods.
Proceedings ArticleDOI
Temporal Link Prediction Using Time Series of Quasi-Local Node Similarity Measures
Alper Ozcan,Sule Gunduz Oguducu +1 more
TL;DR: A novel link prediction method based on NARX Neural Network for evolving networks that combines time information with node similarities and node connectivities improves the link prediction performance to a large extent.
Journal ArticleDOI
Fast asynchronous updating algorithms for k-shell indices
Yan-Li Lee,Tao Zhou +1 more
TL;DR: This paper proposes two algorithms to select nodes and update their intermediate values towards the k- shell indices, which can help in accelerating the convergence of the calculation of k-shell indices.
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.