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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Link prediction in multiplex online social networks.
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Predicting links in ego-networks using temporal information
TL;DR: This work defines several features to capture different kinds of temporal information and applies machine learning methods to combine these various features and improve the quality of the prediction of links among egos’ neighbors.
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Detecting Prosumer-Community Groups in Smart Grids From the Multiagent Perspective
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Predicting missing links and identifying spurious links via likelihood analysis
TL;DR: An algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network is used.
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