Link community detection using generative model and nonnegative matrix factorization

Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. While most of the existing approaches have focused on discovering communities of nodes, recent studies have shown the advantages and uses of link community discovery in networks. Generative models provide a promising class of techniques for the identification of modular structures in networks, but most generative models mainly focus on the detection of node communities rather than link communities. In this work, we propose a generative model, which is based on the importance of each node when forming links in each community, to describe the structure of link communities. We proceed to fit the model parameters by taking it as an optimization problem, and solve it using nonnegative matrix factorization. Thereafter, in order to automatically determine the number of communities, we extend the above method by introducing a strategy of iterative bipartition. This extended method not only finds the number of communities all by itself, but also obtains high efficiency, and thus it is more suitable to deal with large and unexplored real networks. We test this approach on both synthetic benchmarks and real-world networks including an application on a large biological network, and compare it with two highly related methods. Results demonstrate the superior performance of our approach over competing methods for the detection of link communities.This work is supported by Major State Basic Research Development Program of China (2013CB329301), National Natural Science Foundation of China (61303110, 61133011, 61373053, 61070089, 61373165, 61202308), PhD Programs Foundation of Ministry of Education of China (20130032120043), Open Project Program of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education (93K172013K02), Innovation Foundation of Tianjin University (60302034), the TECHNO II project within Erasmus Mundus Programme of European Union, and the China Scholarship Council (award to Dongxiao He for one year's study abroad at Washington University in St Louis). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Genetic algorithm with local search for community mining in complex networks

Detecting communities from complex networks has triggered considerable attention in several application domains. Targeting this problem, a local search based genetic algorithm (GALS) which employs a graph-based representation (LAR) has been proposed in this work. The core of the GALS is a local search based mutation technique. Aiming to overcome the drawbacks of the existing mutation methods, a concept called marginal gene has been proposed, and then an effective and efficient mutation method, combined with a local search strategy which is based on the concept of marginal gene, has also been proposed by analyzing the modularity function. Moreover, in this paper the percolation theory on ER random graphs is employed to further clarify the effectiveness of LAR presentation; A Markov random walk based method is adopted to produce an accurate and diverse initial population; the solution space of GALS will be significantly reduced by using a graph based mechanism. The proposed GALS has been tested on both computer-generated and real-world networks, and compared with some competitive community mining algorithms. Experimental result has shown that GALS is hig y effective and efficient for discovering community structure.This work was supported by National Natural Science Foundation of China under Grant Nos. 60873149, 60973088, National High-Tech Research and Development Plan of China under Grant No. 2006AA10Z245, Open Project Program of the National Laboratory of Pattern Recognition, and BRIDGING THE GAP Erasmus Mundus project of EU. We would like to thank Mark Newman for providing us with the source code of algorithms FN and GN, and some real-world network data

A sparse Bayesian learning method for structural equation model-based gene regulatory network inference

Gene regulatory networks (GRNs) are underlying networks identified by interactive relationships between genes. Reconstructing GRNs from massive genetic data is important for understanding gene functions and biological mechanism, and can provide effective service for medical treatment and genetic research. A series of artificial intelligence based methods have been proposed to infer GRNs from both gene expression data and genetic perturbations. The accuracy of such algorithms can be better than those models that just consider gene expression data. A structural equation model (SEM), which provides a systematic framework integrating both types of gene data conveniently, is a commonly used model for GRN inference. Considering the sparsity of GRNs, in this paper, we develop a novel sparse Bayesian inference algorithm based on Normal-Equation-Gamma (NEG) type hierarchical prior (BaNEG) to infer GRNs modeled with SEMs more accurately. First, we reparameterize an SEM as a linear type model by integrating the endogenous and exogenous variables; Then, a Bayesian adaptive lasso with a three-level NEG prior is applied to deduce the corresponding posterior mode and estimate the parameters. Simulations on synthetic data are run to compare the performance of BaNEG to some state-of-the-art algorithms, the results demonstrate that the proposed algorithm visibly outperforms the others. What’s more, BaNEG is applied to infer underlying GRNs from a real data set composed of 47 yeast genes from Saccharomyces cerevisiae to discover potential relationships between genes

Capture and sorting of multiple cells by polarization-controlled three-beam interference

For the capture and sorting of multiple cells, a sensitive and highly efficient polarization-controlled three-beam interference set-up has been developed. With the theory of superposition of three beams, simulations on the influence of polarization angle upon the intensity distribution and the laser gradient force change with different polarization angles have been carried out. By controlling the polarization angle of the beams, various intensity distributions and different sizes of dots are obtained. We have experimentally observed multiple optical tweezers and the sorting of cells with different polarization angles, which are in accordance with the theoretical analysis. The experimental results have shown that the polarization angle affects the shapes and feature sizes of the interference patterns and the trapping force

Effect of Wu Zhi San supplementation in LPS-induced intestinal inflammation and barrier damage in broilers

Intestinal inflammation and barrier damage can inhibit the absorption and transportation of nutrients in the small intestine, and lead to various chronic diseases. Wu Zhi San (WZS) is a traditional Chinese formula composed of Schisandrae, Anemarrhenae, Lonicerae, and Glycyrrhizae that was made to cure intestinal inflammation and barrier damage in broilers. To evaluate the protective effect of WZS on intestinal inflammation and barrier damage of broilers under lipopolysaccharide (LPS) stress, a total of 200 one-day-old broilers were randomly divided into five groups, namely, the CON group, LPS group, and three WZS groups (WZS-H, WZS-M, and WZS-L). The groups were designed for stress phase I (days 15, 17, 19, and 21) and stress phase II (days 29, 31, 33, and 35). The protective effect of WZS on the intestinal tract was evaluated by measuring the levels of serum myeloperoxidase (MPO), diamine oxidase (DAO), super oxide dismutase (SOD), and serum D-lactate (D-LA) and the expression of inflammatory factors in jejunum. The results showed that the diet supplemented with WZS could significantly reduce serum MPO, DAO, and D-LA levels and jejunal CD in broilers (p < 0.05), increase serum SOD levels and jejunal VH (p < 0.05), significantly downregulate the expression of NF-κB, TLR4, MyD88, and inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-10), and upregulate Claudin-1, Occludin-1, and ZO-1 in broiler jejunum mucosa (p < 0.05). On the other hand, WZS could significantly reduce the protein expression of NF-κB (p65) in broiler jejunum (p < 0.05). These results indicate that supplementing WZS in the diet can reduce intestinal inflammation and alleviate intestinal barrier damage, and by inhibiting the NF-κB/TLR4/MyD88 signaling pathway, supplementation with WZS intervenes in LPS-induced stress injury in broilers

Determination of beam incidence conditions based on the analysis of laser interference patterns

Beam incidence conditions in the formation of two-, three- and four-beam laser interference patterns are presented and studied in this paper. In a laser interference lithography (LIL) process, it is of importance to determine and control beam incidence conditions based on the analysis of laser interference patterns for system calibration as any slight change of incident angles or intensities of beams will introduce significant variations of periods and contrasts of interference patterns. In this work, interference patterns were captured by a He-Ne laser interference system under different incidence conditions, the pattern period measurement was achieved by cross-correlation with, and the pattern contrast was calculated by image processing. Subsequently, the incident angles and intensities of beams were determined based on the analysis of spatial distributions of interfering beams. As a consequence, the relationship between the beam incidence conditions and interference patterns is revealed. The proposed method is useful for the calibration of LIL processes and for reverse engineering applications

Multi-Task Recommendations with Reinforcement Learning

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL's compatibility and transferability across various MTL models.Comment: TheWebConf202