The Evolving Bipartite Network and Semi-Bipartite Network Models with Adjustable Scale and Hybrid Attachment Mechanisms

The bipartite graph structure exists in the connections of many objects in the real world, and the evolving modeling is a good method to describe and understand the generation and evolution within various real complex networks. Previous bipartite models were proposed to mostly explain the principle of attachments, and ignored the diverse growth speed of nodes of sets in different bipartite networks. In this paper, we propose an evolving bipartite network model with adjustable node scale and hybrid attachment mechanisms, which uses different probability parameters to control the scale of two disjoint sets of nodes and the preference strength of hybrid attachment respectively. The results show that the degree distribution of single set in the proposed model follows a shifted power-law distribution when parameter r and s are not equal to 0, or exponential distribution when r or s is equal to 0. Furthermore, we extend the previous model to a semi-bipartite network model, which embeds more user association information into the internal network, so that the model is capable of carrying and revealing more deep information of each user in the network. The simulation results of two models are in good agreement with the empirical data, which verifies that the models have a good performance on real networks from the perspective of degree distribution. We believe these two models are valuable for an explanation of the origin and growth of bipartite systems that truly exist.

An MDL approach to efficiently discover communities in bipartite network

An minimum description length(MDL) criterion is proposed to choose a good partition for a bipartite network. A heuristic algorithm based on combination theory is presented to approach the optimal partition. As the heuristic algorithm automatically searches for the number of partitions, no user intervention is required. Finally, experiments are conducted on various datasets, and the results show that our method generates higher quality results than the state-of-art methods, cross-association and bipartite, recursively induced modules. Experiment results also show the good scalability of the proposed algorithm. The method is applied to traditional Chinese medicine(TCM) formula and Chinese herbal network whose community structure is not well known, and found that it detects significant and it is informative community division.

A uniform framework of projection and community detection for one-mode network in bipartite networks

Projection is a widely used method in bipartite networks. However, each projection has a specific application scenario and differs in the forms of mapping for bipartite networks. In this paper, inspired by the network-based information exchange dynamics, we propose a uniform framework of projection. Subsequently, an information exchange rate projection based on the nature of community structures of a network （named IERCP） is designed to detect community structures of bipartite networks. Results from the synthetic and real-world networks show that the IERCP algorithm has higher performance compared with the other projection methods. It suggests that the IERCP may extract more information hidden in bipartite networks and minimize information loss.

Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current research.In this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is proposed.The edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users.RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender system.We verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.

A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network

Grid-based recommendation algorithms view users and items as abstract nodes,and the information utilised by the algorithm is hidden in the selection relationships between users and items.Although these relationships can be easily handled,much useful information is overlooked,resulting in a less accurate recommendation algorithm.The aim of this paper is to propose improvements on the standard substance diffusion algorithm,taking into account the influence of the user’s rating on the recommended item,adding a moderating factor,and optimising the initial resource allocation vector and resource transfer matrix in the recommendation algorithm.An average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation results.Experiments are conducted on the MovieLens training dataset,and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.

A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

As one large class of non-coding RNAs （ncRNAs）, long ncRNAs （IneRNAs） have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRN^protein bipartite network inference （LPBNI）. LPBNI aims to identify potential lncRNA-interacting proteins, by making full use of the known IncRNA-protein interactions. Leave-one-out cross validation （LOOCV） test shows that LPBNI significantly outperforms other network-based methods, including random walk （RWR） and protein-based collaborative filtering （ProCF）. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA-interacting proteins.

Chimera states in bipartite networks of FitzHugh-Nagumo oscillators

Chimera states consisting of spatially coherent and incoherent domains have been observed in differ- ent topologies such as rings, spheres, and complex networks. In this paper, we investigate bipartite networks of nonlocally coupled FitzHugh-Nagumo （FHN） oscillators in which the units are allocated evenly to two layers, and FHN units interact with each other only when they are in different lay- ers. We report the existence of chimera states in bipartite networks. Owing to the interplay between chimera states in the two layers, many types of chimera states such as in-phase chimera states, an- tiphase chimera states, and out-of-phase chimera states are classified. Stability diagrams of several typical chimera states in the coupling strength-coupling radius plane, which show strong multistability of chimera states, are explored.

BMTK： a toolkit for determining modules in biological bipartite networks

Background： Module detection is widely used to analyze and visualize biological networks. A number of methods and tools have been developed to achieve it. Meanwhile, bipartite module detection is also very useful for mining and analyzing bipartite biological networks and a few methods have been developed for it. However, there is few user- friendly toolkit for this task. Methods： To this end, we develop an online web toolkit BMTK, which implements seven existing methods. Results： BMTK provides a uniform operation platform and visualization function, standardizes input and output format, and improves algorithmic structure to enhance computing speed. We also apply this toolkit onto a drug-target bipartite network to demonstrate its effectiveness. Conclusions： BMTK will be a powerful tool for detecting bipartite modules in diverse bipartite biological networks. Availability： The web application is freely accessible at http：//www.zhanglabtools.net/BMTK.

Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases

Purpose： This study aims to answer the question to what extent different types of networks can be used to predict future co-authorship among authors.Design/methodology/approach： We compare three types ot networks： unwelgntea networks, in which a link represents a past collaboration; weighted networks, in which links are weighted by the number of joint publications; and bipartite author-publication networks. The analysis investigates their relation to positive stability, as well as their potential in predicting links in future versions of the co-authorship network. Several hypotheses are tested.Findings： Among other results, we find that weighted networks do not automatically lead to better predictions. Bipartite networks, however, outperform unweighted networks in almost all cases. Research limitations： Only two relatively small case studies are considered Practical implications： The study suggests that future link prediction studies on networks should consider using the bipartite network as a training network. Originality/value： This is the first systematic comparison of unweighted, weighted, and bipartite training networks in link prediction.

Personalized Recommendation Algorithm Based on Rating System and User Interest Association Network

In most available recommendation algorithms, especially for rating systems, almost all the high rating information is utilized on the recommender system without using any low-rating information, which may include more user information and lead to the accuracy of recommender system being reduced. The paper proposes a algorithm of personalized recommendation (UNP algorithm) for rating system to fully explore the similarity of interests among users in utilizing all the information of rating data. In UNP algorithm, the similarity information of users is used to construct a user interest association network, and a recommendation list is established for the target user with combining the user interest association network information and the idea of collaborative filtering. Finally, the UNP algorithm is compared with several typical recommendation algorithms (CF algorithm, NBI algorithm and GRM algorithm), and the experimental results on Movielens and Netflix datasets show that the UNP algorithm has higher recommendation accuracy.

A study of fault detection way for computer networks based on multiple stages

To relieve traliic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered alter multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method.

An improved recommendation algorithm via weakening indirect linkage effect

We propose an indirect-link-weakened mass diffusion method（IMD）, by considering the indirect linkage and the source object heterogeneity effect in the mass diffusion（MD） recommendation method. Experimental results on the MovieLens, Netflix, and RYM datasets show that, the IMD method greatly improves both the recommendation accuracy and diversity, compared with a heterogeneity-weakened MD method（HMD）, which only considers the source object heterogeneity. Moreover, the recommendation accuracy of the cold objects is also better elevated in the IMD than the HMD method. It suggests that eliminating the redundancy induced by the indirect linkages could have a prominent effect on the recommendation efficiency in the MD method.

Deciphering microeukaryotic–bacterial co-occurrence networks in coastal aquaculture ponds

Microeukaryotes and bacteria are key drivers of primary productivity and nutrient cycling in aquaculture ecosystems.Although their diversity and composition have been widely investigated in aquaculture systems,the co-occurrence bipartite network between microeukaryotes and bacteria remains poorly understood.This study used the bipartite network analysis of high-throughput sequencing datasets to detect the co-occurrence relationships between microeukaryotes and bacteria in water and sediment from coastal aquaculture ponds.Chlorophyta and fungi were dominant phyla in the microeukaryotic–bacterial bipartite networks in water and sediment,respectively.Chlorophyta also had overrepresented links with bacteria in water.Most microeukaryotes and bacteria were classified as generalists,and tended to have symmetric positive and negative links with bacteria in both water and sediment.However,some microeukaryotes with high density of links showed asymmetric links with bacteria in water.Modularity detection in the bipartite network indicated that four microeukaryotes and twelve uncultured bacteria might be potential keystone taxa among the module connections.Moreover,the microeukaryotic–bacterial bipartite network in sediment harbored significantly more nestedness than that in water.The loss of microeukaryotes and generalists will more likely lead to the collapse of positive co-occurrence relationships between microeukaryotes and bacteria in both water and sediment.This study unveils the topology,dominant taxa,keystone species,and robustness in the microeukaryotic–bacterial bipartite networks in coastal aquaculture ecosystems.These species herein can be applied for further management of ecological services,and such knowledge may also be very useful for the regulation of other eutrophic ecosystems.

Detecting Communities by Revised Max-flow Method in Networks

A ubiquitous phenomenon in networks is the presence of communities within which the network connections are dense and between which they are sparser.This paper proposes a max-flow algorithm in bipartite networks to detect communities in general networks.Firstly,we construct a bipartite network in accordance with a general network and derive a revised max-flow problem in order to uncover the community structure.Then we present a local heuristic algorithm to find the optimal solution of the revised max-flow problem.This method is applied to a variety of real-world and artificial complex networks,and the partition results confirm its effectiveness and accuracy.

The structure of natural microbial enemy-victim networks

Introduction:The persistence of generalists and specialists is a topical question in community ecology and results from both ecological and evolutionary processes.At fine taxonomical scales,ecological specialisation,i.e.organisms preferentially exploiting a subset of available habitats,is thought to be a driver promoting niche diversity.It is not clear,however,how different mechanisms interact to shape specialist-generalist coexistence.Methods:We reconstruct the structure of five bacteria-phage networks from soil isolates,and perform an analysis of the relationships between host phylogenetic diversity,parasite specialism,and parasite performance.Results:We show that the co-occurrence of species on a continuum of specialism/generalism is influenced by niche overlap,phage impact on bacterial hosts,and host phylogenetic structure.In addition,using a null-model analysis we show that infection strategies of the phages have more explanatory power than bacterial defenses on key structural features of these antagonistic communities.Conclusions:We report that generalists have more impact on their hosts than specialists,even when the phylogenetic heterogeneity of hosts is controlled for.We discuss our results in the light of their implications for the evolution of biotic interactions.

ECOLOGICAL NETWORKS IN AGROECOSYSTEMS: APPROACHES AND APPLICATIONS

Complex network theory has been increasingly used in various research areas,including agroecosystems.This paper summarizes the basic concepts and approaches commonly used in complex network theory,and then reviews recent studies on the applications in agroecosystems of three types of common ecological networks,i.e.,food webs,pollination networks and microbial co-occurrence networks.In general,agricultural intensification is considered to be a key driver of the change of agroecosystems.It causes the simplification of landscape,leads to the loss of biocontrol through cascading effect in food webs,and also reduces the complexity and connectance of soil food webs.For pollination networks,agricultural intensification impaired the robustness by reducing specialization and enhancing generality.The microbial co-occurrence networks with high connectance and low modularity generally corresponded to high efficiency in utilization of nutrients,and high resistance to crop pathogens.This review aims to show the readers the advances of ecological networks in agroecosystems and inspire the researchers to conduct their studies in a new network perspective.

Plant and fungal species interactions differ between aboveground and belowground habitats in mountain forests of eastern China

Plant and fungal species interactions drive many essential ecosystem properties and processes;however,how these interactions differ between aboveground and belowground habitats remains unclear at large spatial scales.Here,we surveyed 494 pairwise fungal communities in leaves and soils by Illumina sequencing,which were associated with 55 woody plant species across more than 2,000-km span of mountain forests in eastern China.The relative contributions of plant,climate,soil and space to the variation of fungal communities were assessed,and the plant-fungus network topologies were inferred.Plant phylogeny was the strongest predictor for fungal community composition in leaves,accounting for 19.1%of the variation.In soils,plant phylogeny,climatic factors and soil properties explained 9.2%,9.0%and 8.7%of the variation in soil fungal community,respectively.The plant-fungus networks in leaves exhibited significantly higher specialization,modularity and robustness(resistance to node loss),but less complicated topology(e.g.,significantly lower linkage density and mean number of links)than those in soils.In addition,host/fungus preference combinations and key species,such as hubs and connectors,in bipartite networks differed strikingly between aboveground and belowground samples.The findings provide novel insights into cross-kingdom(plant-fungus)species co-occurrence at large spatial scales.The data further suggest that community shifts of trees due to climate change or human activities will impair aboveground and belowground forest fungal diversity in different ways.

Active probing based Internet service fault management in uncertain and noisy environment

In Internet service fault management based on active probing, uncertainty and noises will affect service fault management. In order to reduce the impact, challenges of Internet service fault management are analyzed in this paper. Bipartite Bayesian network is chosen to model the dependency relationship between faults and probes, binary symmetric channel is chosen to model noises, and a service fault management approach using active probing is proposed for such an environment. This approach is composed of two phases： fault detection and fault diagnosis. In first phase, we propose a greedy approximation probe selection algorithm （GAPSA）, which selects a minimal set of probes while remaining a high probability of fault detection. In second phase, we propose a fault diagnosis probe selection algorithm （FDPSA）, which selects probes to obtain more system information based on the symptoms observed in previous phase. To deal with dynamic fault set caused by fault recovery mechanism, we propose a hypothesis inference algorithm based on fault persistent time statistic （FPTS）. Simulation results prove the validity and efficiency of our approach.