Evidence map›Paper›PMID 33265453›Full record

ArticleEntropy (Basel, Switzerland)2018

Quantifying the Effects of Topology and Weight for Link Prediction in Weighted Complex Networks.

Bo Liu, Shuang Xu, Ting Li, Jing Xiao, Xiao-Ke Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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.

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

4 citing papers in PubMed.

  1. Article
  2. Hyper-Null Models and Their Applications.Entropy (Basel, Switzerland) · 2023
    Article
  3. Article
  4. Article
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

5 authors.

Bo LiuCollege of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China.
Shuang XuCollege of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China.
Ting LiCollege of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China.
Jing XiaoCollege of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China.
Xiao-Ke XuCollege of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In weighted networks, both link weight and topological structure are significant characteristics for link prediction. In this study, a general framework combining null models is proposed to quantify the impact of the topology, weight correlation and statistics on link prediction in weighted networks. Three null models for topology and weight distribution of weighted networks are presented. All the links of the original network can be divided into strong and weak ties. We can use null models to verify the strong effect of weak or strong ties. For two important statistics, we construct two null models to measure their impacts on link prediction. In our experiments, the proposed method is applied to seven empirical networks, which demonstrates that this model is universal and the impact of the topology and weight distribution of these networks in link prediction can be quantified by it. We find that in the

Indexed as

link predictionnull modelsweighted networks

Identifiers

PMID33265453
PMCPMC7512883

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.