ArticleComputational intelligence and neuroscience2022
A Meta-Path-Based Evaluation Method for Enterprise Credit Risk.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- A Meta-Path-Based Evaluation Method for Enterprise Credit Risk.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, small and medium-sized enterprises (SMEs) have become an essential part of the national economy. With the increasing number of such enterprises, how to evaluate their credit risk becomes a hot issue. Unlike big enterprises with massive data to analyse, it is hard to find enough primary information of SMEs to assess their financial status, which makes the credit risk evaluation result less accurate. Limited by the lack of primary data, how to infer SMEs' credit risk from secondary data, such as information about their upstream, downstream, parent, and subsidiary enterprises, attracts big attention from industry and academy. Targeting on accurately evaluating the credit risk of the SME, in this study, we exploit the representative power of the information network on various kinds of SME entities and SME relationships to solve the problem. With that, a heterogeneous information network of SMEs is built to mine enterprise's secondary information. Furthermore, a novel feature named meta-path feature is proposed to measure the credit risk, which makes us able to evaluate the financial status of SMEs from various perspectives. Experiments show that our proposed meta-path feature is effective to identify SMEs with credit risks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.