ArticlePloS one2016
Link Prediction in Weighted Networks: A Weighted Mutual Information Model.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Chronic Treatment with Angiotensin-(1-7) Improves Metabolism by Modulating Adipose Tissue and Oxidative Stress in Mice.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2025Article
- Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data.Entropy (Basel, Switzerland) · 2022Article
- An information theoretic approach to link prediction in multiplex networks.Scientific reports · 2021Article
- Mutual information model for link prediction in heterogeneous complex networks.Scientific reports · 2017Article
- Weight prediction in complex networks based on neighbor set.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The link-prediction problem is an open issue in data mining and knowledge discovery, which attracts researchers from disparate scientific communities. A wealth of methods have been proposed to deal with this problem. Among these approaches, most are applied in unweighted networks, with only a few taking the weights of links into consideration. In this paper, we present a weighted model for undirected and weighted networks based on the mutual information of local network structures, where link weights are applied to further enhance the distinguishable extent of candidate links. Empirical experiments are conducted on four weighted networks, and results show that the proposed method can provide more accurate predictions than not only traditional unweighted indices but also typical weighted indices. Furthermore, some in-depth discussions on the effects of weak ties in link prediction as well as the potential to predict link weights are also given. This work may shed light on the design of algorithms for link prediction in weighted networks.
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Registered trials
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