Scientific Paper Discrimination Method Research Based On Laplacian Spectrum Analysis

Academic papers expressed by natural language and formal language papers are the most important tools that the human preserve and disseminate the knowledge. Today, however, there are many poor academic papers and even inauthentic those take up academic publication resources and pollute of human knowledge. These poor and inauthentic papers artificially produced or automatically generated by algorithms have a common feature which is standard on grammar with no problems, but not obscure and even pointless in semantics. These poor quality or inauthentic papers should have essential differences with serious and high level academic papers. Survey the essential differences, and using them to initially discriminate their papers is the main contents of this article. Through this research, we can more in-depth understand the structural features of human knowledge mainly expressed by the natural language. In addition, from a practical point of view, it will be a very practical and great value work that paper reviewers' valuable time is not to waste in the inauthentic academic papers if large quantities of papers on the manuscript can be discriminated for a more reliable initial.As a real complex network, the small world and scale-free characteristics of language network have been proved by Chinese and foreign scholars. According to the complex network characteristics of the language network, we can presume that the word co-occurrence networks of dissertation papers are more likely the characteristics of random networks, while the real papers are more inclined to the characteristics of the small world network or the scale-free network. While in the study of characteristics of complex networks, some scholars apply the Laplacian spectrum distribution of graph theory in network topology structure from the geometric view, and find that the Laplacian spectrum distributions with the random network, the small world network and the scale-free network are significantly different.This paper takes the word co-occurrence network of scientific papers as the object of study. We use the Laplacian spectrum analysis method to study the structures of the word co-occurrence networks. Based on the comparative study of the Laplace spectral characteristics of scientific papers: Laplace eigenvalue distribution, Laplace spectral density distribution and the Laplace extreme eigenvalues, we can find the essential different characteristics of the two types of scientific papers identified by the Laplacian spectrum, and that, these differences can be used to design Laplacian spectrum screening method to achieve scientific Automatic paper screening.In this paper, we use the Laplacian spectrum discriminating method to plot and in-depth analysis of the Laplace spectra graphs of the authenticity of the collected scientific papers samples. The papers samples are MIS Quarter papers, accepted and not accepted papers of International Conference on Management Science and Engineering, and the SCI engine pseudo-random generated papers. We select all 100 papers of every type of the four samples and comparative investigate their Laplace spectra. The study of the paper discovers that there are significant differences in spectral distribution which can be prove that Laplacian spectral characteristics of the word co-occurrence network can be used to identify the authenticity of scientific papers.