Active Semisupervised Clustering Algorithm with Label Propagation for Imbalanced and Multidensity Datasets

The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced

Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method

Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods

Active semi-supervised community detection based on must-link and cannot-link constraints.

Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods

Untargeted Metabolomics on Skin Mucus Extract of Channa argus against Staphylococcus aureus: Antimicrobial Activity and Mechanism

Microbial contamination is one of the most common food safety issues that lead to food spoilage and foodborne illness, which readily affects the health of the masses as well as gives rise to huge economic losses. In this study, Channa argus was used as a source of antimicrobial agent that was then analyzed by untargeted metabolomics for its antibacterial mechanism against Staphylococcus aureus. The results indicated that the skin mucus extract of C. argus had great inhibitory action on the growth of S. aureus, and the morphology of S. aureus cells treated with the skin mucus extract exhibited severe morphological damage under scanning electron microscopy. In addition, metabolomics analysis revealed that skin mucus extract stress inhibited the primary metabolic pathways of S. aureus by inducing the tricarboxylic acid cycle and amino acid biosynthesis, which further affected the normal physiological functions of biofilms. In conclusion, the antimicrobial effect of the skin mucus extract is achieved by disrupting cell membrane functions to induce an intracellular metabolic imbalance. Hence, these results conduce to amass novel insights into the antimicrobial mechanism of the skin mucus extract of C. argus against S. aureus

Algorithm 4: Similarity computation algorithm based on random walk.

<p>Algorithm 4: Similarity computation algorithm based on random walk.</p

Collaboration network of scientists at the Santa Fe Institute.

<p>(a) The ground truth community structure; (b) The community structure detected by the proposed algorithm; (c) The community structure obtained by Fast<i>Q</i>; (d) The community structure aggregated from 30 results of LPA; (e) The first-level community structure extracted by Infohiermap; (f) The second-level community structure extracted by Infohiermap; (g) The community structure identified by PPC.</p