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Network node prediction research currently focuses on the prediction of source nodes and hidden nodes, but lacks research on prediction of new nodes. This paper took the relational network of papers and keywords as the research object, used keyword combination to predict the emergence of new papers, and carried out the prediction research of new nodes . First, this essay projected and weighted the paper-keyword bipartite network into a keyword relational network, and then used the possib ility of keyword combination to predict the emergence of new papers in the future. There are two aspects to consider to calculate this possibility. One is simila rity, which indicates the tendency of keywords to co-occur ; and the other is mutual exclusion, which describes the tendency of k eywords to exclude each othe r. For example, two keywords with a high degree of concord rarely appear at the same time. Collected the papers and keywords information of the journal to con struct the dataset, it verifies the proposed new paper prediction algorithm, and compared with the existing algorithms . The results show that the node prediction algorithm proposed has better prediction effect. [ABSTRACT FROM AUTHOR]

Network node prediction research currently focuses on the prediction of source nodes and hidden nodes, but lacks research on prediction of new nodes. This paper took the relational network of papers and keywords as the research object, used keyword combination to predict the emergence of new papers, and carried out the prediction research of new nodes. First, this essay projected and weighted the paper-keyword bipartite network into a keyword relational network, and then used the possibility of keyword combination to predict the emergence of new papers in the future. There are two aspects to consider to calculate this possibility. One is similarity, which indicates the tendency of keywords to co-occur; and the other is mutual exclusion, which describes the tendency of keywords to exclude each other. For example, two keywords with a high degree of concord rarely appear at the same time. Collected the papers and keywords information of the journal to construct the dataset, it verifies the proposed new paper prediction algorithm, and compared with the existing algorithms . The results show that the node prediction algorithm proposed has better prediction effect. [ABSTRACT FROM AUTHOR]

According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA. [ABSTRACT FROM AUTHOR]

In this paper, the statistical property of the bipartite network, namely clustering coefficient C4 is taken into account and be embedded into the collaborative filtering (CF) algorithm to improve the algorithmic accuracy and diversity. In the improved CF algorithm, the user similarity is defined by the mass diffusion process, and we argue that the object clustering C4 of the bipartite network should be considered to improve the user similarity measurement. The statistical result shows that the clustering coefficient of the MovieLens data approximately has Poisson distribution. By considering the clustering effects of object nodes, the numerical simulation on a benchmark data set shows that the accuracy of the improved algorithm, measured by the average ranking score and precision, could be improved 15.3 and 13.0%, respectively, in the optimal case. In addition, numerical results show that the improved algorithm can provide more diverse recommendation results, for example, when the recommendation list contains 20 objects, the diversity, measured by the hamming distance, is improved by 28.7%. Since all of the real recommendation data are evolving with time, this work may shed some light on the adaptive recommendation algorithm according to the statistical properties of the user-object bipartite network. [ABSTRACT FROM AUTHOR]

In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their degree and the user's tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated. The numerical results indicate that the presented algorithm could improve the accuracy, measured by the average ranking score. More importantly, we find that when the data is sparse, the algorithm should give more recommendation power to the objects whose degrees are close to the user's tastes, while when the data becomes dense, it should assign more power on the objects whose degrees are significantly different from user's tastes. [ABSTRACT FROM AUTHOR]

Neighborhood-based top-N recommendation techniques for implicit users ' inte raction data are ranking models, of which similarity functions are the important ingredients. Traditional similarity functions suffer from two major issues: sparse data and high dimensional data. Sparse data hinder the recommendation system from scoring on smooth neighborhood, high dimensional data causes the curse of dimensionality problem. This paper proposed a representation-based approach named multi object representation learning ( MO) for recommendation, MO was a bipartite node representation learning algorithm, which embedded the different level of network structures and item ordering information into nodes representations to help leverage the recommendation performance. Experimental results on three real data sets of different scales show that the algorithm has higher accuracy and recall than the commonly used recommendation model based on implicit feedback, especially for large-scale data sets, which can effectively alleviate the problems of matrix sparsity and dimensionality disaster, and improve the recommended performance. [ABSTRACT FROM AUTHOR]

Most recommendation systems based on the network structure suffer from lack of diversity, so that the paper proposed a recommendation system of big data based on multi-weight projection of bipartite network. Firstly, it abstracted the basic information of datasets, and applied items-users lists as an input to Levenshtein distance algorithm to compute similarity of each property. Then, it computed the number of common neighbors of the nodes in the bipartite network, the degree of common neighbors of the nodes in the bipartite network and degree of each node in bipartite network, and also computed the triple weights of each side of the bipartite network. Lastly, it adopted the enhanced bipartite projection technique to abstract the potential links of the bipartite network to realize the link prediction based on similarity. It realized the experiments based on both of big dataset and small dataset. The results show that the proposed algorithm outperforms different kinds of recommendation systems in terms of accuracy and coverage of recommendation, at the same time, it outperforms the other recommendation system based on network structure. [ABSTRACT FROM AUTHOR]

In this paper, we develop a generic algorithm for determining a maximum flow in a semi-bipartite network. This algorithm allows only nodes in N1 to be active. For this reason, it performs pushes on individual arcs having both endpoints in N1 or admissible paths of length 2 whose starting and ending nodes are both contained in N1. The running time of this algorithm is O(n21 m). [ABSTRACT FROM AUTHOR]

The efficiency of traditional user-based collaborative filtering (user-based CF) recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection (RACD) in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly， the user-item rating matrix is mapped into user-item bipartite network. Then， the community information of each user is obtained by using RACD to divide the user-item network. Finally， the items are recommended to the target user according to other users in the same community. Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system. [ABSTRACT FROM AUTHOR]

In this paper, the statistical property, namely degree correlation between users and objects, is taken into account and be embedded into the similarity index of collaborative filtering (CF) algorithm to improve the algorithmic performance. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the presented algorithm, measured by the average ranking score, is improved by 18.19% in the optimal case. The statistical analysis on the product distribution of the user and object degrees indicate that, in the optimal case, the distribution obeys the power-law and the exponential is equal to -2.33. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%. Since all of the real recommendation data evolving with time, this work may shed some light on the adaptive recommendation algorithm which could change its parameter automatically according to the statistical properties of the user-object bipartite network. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user–user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-N similar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we describe the cost scaling algorithm for the minimum cost flow in bipartite networks. The cost scaling algorithm for minimum cost flow problem in general networks, developed by Goldberg and Tarjan, is based on ϵ-optimality conditions. The basic idea behind the cost scaling algorithm for the minimum cost flow in a bipartite network is to perform bipushes from nodes in N1. A bipush is a push over two consecutive admissible arcs. Consequently, a bipush moves the excess from a node in N1 to another node in N1. This approach has all the advantages of the scaling cost algorithm for the minimum cost flow in general networks. Moreover, it has an additional advantage that leads to an improved running time. This additional advantage consists in the fact that, using bipushes instead of pushes, all the nodes in N2 are maintained balanced. Consequently, the running time of the cost scaling algorithm for the minimum cost flow is reduced from O(n2mlog(nB)) to O(n12m log(nB)) when it is applied on bipartite networks. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a recommendation algorithm based on the resource-allocation progresses on bipartite networks. In this model, each node is assigned an attraction that is proportional to the power of its degree, where the exponent is an adjustable parameter that controls the configuration of attractions. In the resource-allocation process, each transmitter distributes its each neighbor a fragment of resource that is proportional to the attraction of the neighbor. Based on a benchmark database, we find that decreasing the attractions that the nodes with higher degrees are assigned can further improve the algorithmic accuracy. More significantly, numerical results show that the optimal configuration of attractions subject to accuracy can also generate more diverse and less popular recommendations. [Copyright &y& Elsevier]

Abstract: Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients and of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks. [Copyright &y& Elsevier]

Background: Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. Results: In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. Conclusion: Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases. [ABSTRACT FROM AUTHOR]

We develop and apply an expanded algorithm for measuring urban connectivity through the lens of the location strategies of producer services firms. Although our algorithm is broadly in the spirit of Taylor's (Geographical Analysis 33(2):181–194, Taylor 2001) interlocking network model, we contend that it takes on board more of the information contained in the location decisions of producer services firms. Our algorithm for calculating one-mode asymmetric city-dyads is inspired by network analysis research on resource allocation dynamics, which we extend with a weighting method to extract some extra information contained in the two-mode city-by-firm network. To this end, the location strategies of firms, which are co-produced by local governments seeking investments, are regarded as processes of 'recommendation'. In this process, producer services' offices are defined as scarce resources to be allocated in the bipartite network. In our analysis, we focus on the location strategies of producer services firms across 106 Chinese cities. Drawing on statistical analysis of the asymmetry in pairs of city-dyads, core cities and peripheral cities are identified. Our results also suggest that provincial capitals tend to link with cities that predominantly house firms with a widely dispersed office network. [ABSTRACT FROM AUTHOR]