Numerous problems in many fields can be solved effectively through the approach of modeling by complex network analysis. Finding key nodes is one of the most important and challenging problems in network analysis. In previous studies, methods have been proposed to identify key nodes. However, they rely mainly on a limited field of local information, lack large-scale access to global information, and are also usually NP-hard. In this paper, a novel entropy and mutual information-based centrality approach (EMI) is proposed, which attempts to capture a far wider range and a greater abundance of information for assessing how vital a node is. We have developed countermeasures to assess the influence of nodes: EMI is no longer confined to neighbor nodes, and both topological and digital network characteristics are taken into account. We employ mutual information to fix a flaw that exists in many methods. Experiments on real-world connected networks demonstrate the outstanding performance of the proposed approach in both correctness and efficiency as compared with previous approaches.

BackgroundMicrobe plays a crucial role in the functional mechanism of an ecosystem. Identification of the interactions among microbes is an important step towards understand the structure and function of microbial communities, as well as of the impact of microbes on human health and disease. Despite the importance of it, there is not a gold-standard dataset of microbial interactions currently. Traditional approaches such as growth and co-culture analysis need to be performed in the laboratory, which are time-consuming and costly. By providing predicted candidate interactions to experimental verification, computational methods are able to alleviate this problem. Mining microbial interactions from mass medical texts is one type of computational methods. Identification of the named entity of bacteria and related entities from the text is the basis for microbial relation extraction. In the previous work, a system of bacteria named entities recognition based on the dictionary and conditional random field was proposed. However, it is inefficient when dealing with large-scale text.ResultsWe implemented bacteria named entity recognition on Spark platform and designed experiments for comparison to verify the correctness and validity of the proposed system. The experimental results show that it can achieve higher F-Measure on the comparison of correctness. Moreover, the predicting speed is much faster than the previous version in large-scale biomedical datasets, and the computational efficiency is improved remarkably by about 3.1 to 6.7 times.ConclusionsThe system for bacteria named entity recognition solves the inefficiency of the previous proposed system on large-scale datasets. The proposed system has good performance in accuracy and scalability.