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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.

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A Review of Relational Machine Learning for Knowledge Graphs

TL;DR: The authors provide a review of how such models can be trained on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph) and discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web.
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Ranking scientific publications with similarity-preferential mechanism

TL;DR: Though this method is only applied to citation networks in this paper, it can be naturally used in many other real systems, such as designing search engines in the World Wide Web and revealing the leaderships in social networks.
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Meta-path based heterogeneous combat network link prediction

TL;DR: A novel integrated methodology framework to predict multiple types of links simultaneously for an HCN, based on meta-path features, is proposed, and the results show that the performance of the HCNMP is superior to those of the baseline methods.
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Connection Discovery Using Big Data of User-Shared Images in Social Media

TL;DR: It is observed that the shared images from users with a follower / followee relationship show relatively higher similarities, and a multimedia big data system that utilizes this observed phenomenon is proposed as an alternative to user- generated tags and social graphs for follower/followee recommendation and gender identification.
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Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm

TL;DR: The intelligent attention allocation link prediction algorithm is proposed to adaptively build attention allocation index (AAI) according to the sparseness of the network and predict the possible friendships between users in different circles.
References
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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.
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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.
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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.
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The meaning and use of the area under a receiver operating characteristic (ROC) curve.

James A. Hanley, +1 more
- 01 Apr 1982 - 
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.