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
Influence of edge weight on node proximity based link prediction methods
TL;DR: This paper revisits the study of the effect of tie weight on link prediction, and proposes two new weighting models namely, min-flow and multiplicative, and performs an analysis of the weak tie theory, and observes that unweighted models are suitable for inter-community link Prediction, and weighted models are suited for intra- community link prediction.
Journal ArticleDOI
Playing the role of weak clique property in link prediction: A friend recommendation model
TL;DR: This work proposes a local friend recommendation (FR) index, utilizing the PWCS phenomenon, which improves the accuracy of link prediction and proposes a mixed friend recommendation index (labelled MFR), which further improves the accuracies of links prediction.
Journal ArticleDOI
A utility-based link prediction method in social networks
TL;DR: Utility analysis is introduced to the link prediction method by considering that individual preferences are the main reason behind the decision to form links, and meanwhile it also focuses on the meeting process that is a latent variable during the process of forming links.
Posted Content
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
TL;DR: SkipGNN predicts molecular interactions by not only aggregating information from direct interactions but also from second-order interactions, which is called skip similarity, and it is shown that unlike popular GNNs, SkipGNN learns biologically meaningful embeddings and performs especially well on noisy, incomplete interaction networks.
Posted Content
An Overview of Distance and Similarity Functions for Structured Data
TL;DR: The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence (AI) in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations as mentioned in this paper.
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.