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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Book ChapterDOI
Link Prediction via Higher-Order Motif Features
TL;DR: This paper proposes a set of features that depend on the patterns or motifs that a pair of nodes occurs in, and experimentally demonstrates that using off-the-shelf classifiers with a well constructed classification dataset results in up to 10 percentage points increase in accuracy over prior topology-based and feature learning methods.
Journal ArticleDOI
Graph regularization weighted nonnegative matrix factorization for link prediction in weighted complex network
TL;DR: This model integrates two types of information: local topology and link weight information, and utilizes the weighted cosine similarity(WCS) method to calculate the weight similarity between local nodes and derives the multiplicative updating rules to learn the parameter of this model.
Journal ArticleDOI
Social Link Prediction in Online Social Tagging Systems
TL;DR: This article proposes latent topic models as a principled way of reducing the dimensionality of such data and capturing the dynamics of collaborative annotation process and proposes three generative processes to model latent user tastes with respect to resources they annotate with metadata.
Journal ArticleDOI
ASCOS++: An Asymmetric Similarity Measure for Weighted Networks to Address the Problem of SimRank
Hung-Hsuan Chen,C. Lee Giles +1 more
TL;DR: This article argues that SimRank and its families, such as P-Rank and SimRank++, fail to capture similar node pairs in certain conditions, and presents new similarity measures ASCOS and ASCOS++ to address the problem.
Journal ArticleDOI
Mining protein interactomes to improve their reliability and support the advancement of network medicine.
TL;DR: An important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed, and a few representative examples of how molecular and clinical data can be integrated to deepen the understanding of pathogenesis are discussed.
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