Evidence map›Paper›PMID 26198206›Full record

ArticleScientific reports2015

Prediction of Links and Weights in Networks by Reliable Routes.

Jing Zhao, Lili Miao, Jian Yang, Haiyang Fang, Qian-Ming Zhang, Min Nie, Petter Holme, Tao Zhou

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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  11. Temporally Factorized Network Modeling for Evolutionary Network Analysis.Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jing ZhaoDepartment of Mathematics, Logistical Engineering University, Chongqing, China.
Lili MiaoCompleX Lab, Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China.
Jian YangDepartment of Mathematics, Logistical Engineering University, Chongqing, China.
Haiyang FangDepartment of Mathematics, Logistical Engineering University, Chongqing, China.
Qian-Ming ZhangCompleX Lab, Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China.
Min NieCompleX Lab, Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China.
Petter HolmeDepartment of Energy Science, Sungkyunkwan University, Suwon, Korea.
Tao Zhou1] CompleX Lab, Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China [2] Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Link prediction aims to uncover missing links or predict the emergence of future relationships from the current network structure. Plenty of algorithms have been developed for link prediction in unweighted networks, but only a few have been extended to weighted networks. In this paper, we present what we call a "reliable-route method" to extend unweighted local similarity indices to weighted ones. Using these indices, we can predict both the existence of links and their weights. Experiments on various real-world networks suggest that our reliable-route weighted resource-allocation index performs noticeably better than others with respect to weight prediction. For existence prediction it is either the highest or very close to the highest. Further analysis shows a strong positive correlation between the clustering coefficient and prediction accuracy. Finally, we apply our method to the prediction of missing protein-protein interactions and their confidence scores from known PPI networks. Once again, our reliable-route method shows the highest accuracy.

Indexed as

AlgorithmsCluster AnalysisHumansProtein Interaction Mapping

Identifiers

PMID26198206
PMCPMC4510530

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.