Bipartite Model Research Articles

A new approach on measuring the knowledge complexity in the view of the bipartite network

ABSTRACT Developed countries are guaranteed to have the ownership of higher knowledge complexity, which is naturally accepted and universally acknowledged. However, the reality may tell a different story. In this article, a brand-new approach is utilized to quantify the knowledge complexity. Within the bipartite network model, based on the Fitness and Complexity algorithm and the matrix-estimation exercise, a couple of indicators are constructed to measure generalized knowledge complexities of countries and technologies. Results illuminate that admittedly knowledge complexities of countries and those of technologies are interrelated, and those established and developed countries, compared with developing ones, do not necessarily own higher knowledge complexity. To be more specific, an increasing number of facts demonstrate that some less-developed countries with less-advanced technologies are progressing in leaps and bounds, for they attach great importance to investing in high-knowledge-complexity technologies.

Markovian Online Matching Algorithms on Large Bipartite Random Graphs

In this paper, we present an approximation of the matching coverage on large bipartite graphs, for local online matching algorithms based on the sole knowledge of the remaining degree of the nodes of the graph at hand. This approximation is obtained by applying the Differential Equation Method to a measure-valued process representing an alternative construction, in which the matching and the graph are constructed simultaneously, by a uniform pairing leading to a realization of the bipartite Configuration Model. The latter auxiliary construction is shown to be equivalent in distribution to the original one. It allows to drastically reduce the complexity of the problem, in that the resulting matching coverage can be written as a simple function of the final value of the process, and in turn, approximated by a simple function of the solution of a system of ODE’s. By way of simulations, we illustrate the accuracy of our estimate, and then compare the performance of an algorithm based on the minimal residual degree of the nodes, to the classical greedy matching.

Influence of Teaching and Course Evaluation of Performing Arts Students Based on Improved Ant Colony Algorithm and Data Fusion

Teaching quality evaluation plays a very important role in the evaluation of teaching and learning in universities, especially in performing arts courses. Therefore, in this study, the application and detailed analysis process of clustering analysis on student performance data is used to finally obtain the guidance of clustering results on teaching and management. A mathematical model of teachers was established using graph theory to transform the evaluation process into a bipartite graph model and to fully utilize the memory capacity, mastery of prior knowledge, adaptability, and collaboration of the ant colony algorithm. Moving back and forth between the two sets of partial graphs completed the evaluation of the teacher teaching process. In addition, the maximum and minimum pheromone strategies were applied in the original ant colony algorithm; the robustness of the algorithm itself was enhanced by making full use of the memory ability and the necessary prior knowledge mastery in solving the teacher teaching evaluation problem. For the unqualified teachers, according to the principle of PDCA (quality environment), regular training, mutual lectures, and further training can be adopted to improve teachers’ teaching levels. Through comparison, it is found that the general and practical courses of art performance majors do not have professional characteristics, and the professional courses are mostly dance and performance courses, while physical education, sports training, and other related courses are insufficient. Through the method of this study, all students are concentrated in the group of teachers with higher satisfaction, and “teaching” can achieve the expected effect and provide theoretical and practical basis for the development of performing arts courses.

UPEC Colonic-Virulence and Urovirulence Are Blunted by Proanthocyanidins-Rich Cranberry Extract Microbial Metabolites in a Gut Model and a 3D Tissue-Engineered Urothelium.

ABSTRACTUropathogenic Escherichia coli (UPEC) ecology-pathophysiology from the gut reservoir to its urothelium infection site is poorly understood, resulting in equivocal benefits in the use of cranberry as prophylaxis against urinary tract infections. To add further understanding from the previous findings on PAC antiadhesive properties against UPEC, we assessed in this study the effects of proanthocyanidins (PAC) rich cranberry extract microbial metabolites on UTI89 virulence and fitness in contrasting ecological UPEC’s environments. For this purpose, we developed an original model combining a colonic fermentation system (SHIME) with a dialysis cassette device enclosing UPEC and a 3D tissue-engineered urothelium. Two healthy fecal donors inoculated the colons. Dialysis cassettes containing 7log10 CFU/mL UTI89 were immersed for 2h in the SHIME colons to assess the effect of untreated (7-day control diet)/treated (14-day PAC-rich extract) metabolomes on UPEC behavior. Engineered urothelium were then infected with dialysates containing UPEC for 6 h. This work demonstrated for the first time that in the control fecal microbiota condition without added PAC, the UPEC virulence genes were activated upstream the infection site, in the gut. However, PAC microbial-derived cranberry metabolites displayed a remarkable propensity to blunt activation of genes encoding toxin, adhesin/invasins in the gut and on the urothelium, in a donor-dependent manner. Variability in subjects’ gut microbiota and ensuing contrasting cranberry PAC metabolism affects UPEC virulence and should be taken into consideration when designing cranberry efficacy clinical trials.IMPORTANCE Uropathogenic Escherichia coli (UPEC) are the primary cause of recurrent urinary tract infections (UTI). The poor understanding of UPEC ecology-pathophysiology from its reservoir–the gut, to its infection site–the urothelium, partly explains the inadequate and abusive use of antibiotics to treat UTI, which leads to a dramatic upsurge in antibiotic-resistance cases. In this context, we evaluated the effect of a cranberry proanthocyanidins (PAC)-rich extract on the UPEC survival and virulence in a bipartite model of a gut microbial environment and a 3D urothelium model. We demonstrated that PAC-rich cranberry extract microbial metabolites significantly blunt activation of UPEC virulence genes at an early stage in the gut reservoir. We also showed that altered virulence in the gut affects infectivity on the urothelium in a microbiota-dependent manner. Among the possible mechanisms, we surmise that specific microbial PAC metabolites may attenuate UPEC virulence, thereby explaining the preventative, yet contentious properties of cranberry against UTI.

Inferring signed networks from preschoolers’ observed parallel and social play

Early childhood is an important developmental period for network formation. However, the observational methods used for measuring young children’s networks present challenges for capturing both positive and negative ties. To overcome these challenges, we explored the use of a bipartite projection backbone model for inferring both negative and positive ties from observational data of children’s play. Using observational data collected in one 3-year-old (N = 17) and one 4-year-old (N = 18) preschool classroom, we examined whether patterns of homophily, triadic closure, and balance in networks inferred using this method matched theoretical and empirical expectations from the early childhood literature. Consistent with this literature, we found that signed networks inferred using a backbone model exhibited gender homophily in positive ties and gender heterophily in negative ties. Additionally, networks inferred from social play exhibited more closed and balanced triads than networks inferred from parallel play. These findings offer evidence of the validity of bipartite projection backbone models for inferring signed networks from preschoolers’ observed play.

Identifying similar-bicliques in bipartite graphs

Bipartite graphs have been widely used to model the relationship between entities of different types, where vertices are partitioned into two disjoint sets/sides. Finding dense subgraphs in a bipartite graph is of great significance and encompasses many applications. However, none of the existing dense bipartite subgraph models consider similarity between vertices from the same side, and as a result, the identified results may include vertices that are not similar to each other. In this paper, we formulate the notion of similar-biclique which is a special kind of biclique where all vertices from a designated side are similar to each other, and aim to enumerate all similar-bicliques. The naive approach of first enumerating all maximal bicliques and then extracting all maximal similar-bicliques from them is inefficient, as enumerating maximal bicliques is time consuming. We propose a backtracking algorithm MSBE to directly enumerate maximal similar-bicliques, and power it by vertex reduction and optimization techniques. Furthermore, we design a novel index structure to speed up a time-critical operation of MSBE, as well as to speed up vertex reduction. Efficient index construction algorithms are also developed. Extensive experiments on 17 bipartite graphs as well as case studies are conducted to demonstrate the effectiveness and efficiency of our model and algorithms.

Intervention strategies for epidemic spreading on bipartite metapopulation networks.

Intervention strategies are of great significance for controlling large-scale outbreaks of epidemics. Since the spread of epidemic depends largely on the movement of individuals and the heterogeneity of the network structure, understanding potential factors that affect the epidemic is fundamental for the design of reasonable intervention strategies to suppress the epidemic. So far, most of previous studies mainly consider intervention strategies on the network composed of a single type of locations, while ignoring the movement behavior of individuals to and from locations that are composed of different types, i.e., residences and public places, which often presents heterogeneous structure. In addition, the transmission rate in public places with different population flows is heterogeneous. Inspired by the above observation, we build a bipartite metapopulation network model and propose intervention strategies based on the importance of public places. With the Markovian Chain approach, we derive the epidemic threshold under intervention strategies. Experimental results show that, compared with the uniform intervention to residences or public places, nonuniform intervention to public places is more effective for suppressing the epidemic with an increased epidemic threshold. Specifically, interventions to public places with large degree can further suppress the epidemic. Our study opens a new path for understanding the spatial epidemic spread and provides guidance for the design of intervention strategies for epidemics in the future.

Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data Analytics.

(1) Background: This study introduces a novel computational approach to examine government capabilities in information intervention for risk management, influential agents in a global information network, and the socioeconomic factors of information-sharing behaviors of the public across regions during the COVID-19 pandemic. (2) Methods: Citation network analysis was employed to gauge the online visibility of governmental health institutions across regions. A bipartite exponential random graph modeling (ERGM) procedure was conducted to measure network dynamics. (3) Results: COVID-19 response agencies in Europe had the highest web impact, whereas health agencies in North America had the lowest. Various stakeholders, such as businesses, non-profit organizations, governments, and educational institutions played a key role in sharing the COVID-19 response by agencies’ information given on their websites. Income inequality and GDP per capita were associated with the high online visibility of governmental health agencies. Other factors, such as population size, an aging population, death rate, and case percentage, did not contribute to the agencies’ online visibility, suggesting that demographic characteristics and health status are not predictors of sharing government resources. (4) Conclusions: A combination of citation network analysis and ERGM helps reveal information flow dynamics and understand the socioeconomic consequences of sharing the government’s COVID-19 information during the pandemic.

Quantum simulator of extended bipartite Hubbard model with broken sublattice symmetry: Magnetism, correlations, and phase transitions

We describe here a quantum simulator of extended bipartite Hubbard model with broken sublattice symmetry. The simulator consists of a structured lateral gate confining two dimensional electrons in a quantum well into artificial minima arranged in a hexagonal lattice. The sublattice symmetry breaking is generated by forming an artificial triangular graphene quantum dot (ATGQD) with zigzag edges. The resulting extended Hubbard model generates tunable ratio of tunneling strength to electron-electron interactions and of sublattice symmetry with control over shape. The validity of the simulator is confirmed for small systems using mean-field and exact diagonalization many-body approaches which show that the ground state changes from a metallic to an antiferromagnetic (AF) phase by varying the distance between sites or depth of the confining potential. The one-electron spectrum of these triangular dots contains a macroscopically degenerate shell at the Fermi level. The shell persists at the mean-field level for weak interactions (metallic phase) but disappears for strong interactions, in the AF phase. We determine the effects of electron-electron interactions on the ground state, the total spin, and the excitation spectrum as a function of filling of the ATGQD. We find that the half-filled charge neutral shell leads to a partially spin polarized state in both metallic and AF regimes in accordance with Liebs theorem. In both regimes a relatively large gap separates the spin polarized ground state to the first excited many-body state at half filling of the degenerate shell. By adding or removing an electron, this gap drops dramatically, and alternate total spin states emerge with energies nearly degenerate to a spin polarized ground state.

Work costs and operating regimes for different manners of system-reservoir interactions via collision model

In this work, we study effects of different types of system-reservoir interactions on work costs and operating regimes of thermal machines by considering a quantum system consisting of two subsystems embedded in both independent and common reservoirs. The model allows us to make a contrast between three configurations of system-reservoir interactions, namely, the three-body one, the two-body one with and without intrasystem interaction between two subsystems. After establishing general formulations of thermodynamics quantities, we derive specific forms of heat and work with respect to these three configurations based on a model with two coupled qubits. It is shown that both the amount and sign of work are closely related to ways of system-reservoir interactions, by which six types of operating regimes of machines are constructed for a given setting. We find that different modes of system-reservoir interactions lead to different numbers of operating regimes of machines on the one hand, and on the other hand machines of the same kinds can appear in different scenarios of system-reservoir interactions, but which one is superior over others relies on intervals of parameters. A possible implementation of the setup based on the platform of circuit quantum electrodynamics is discussed briefly. We then generalize the bipartite model to multipartite case and derive the corresponding formulations of thermodynamics quantities. Our results indicate that interacting manners of system-reservoir play an important role in modifying thermodynamics process and can thus be utilized in designing quantum thermal machines with requisite functions.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

Coverage Enhancement Algorithm for WSNs Based on Vampire Bat and Improved Virtual Force

Coverage enhancement is an important strategy to avoid coverage holes caused by the random deployment of sensor nodes. Given the harsh physical environments of wireless sensor networks (WSNs), which impede the energy supplement and recovery of sensor nodes, the motivation of our research is to repair the coverage holes and reduce the energy consumption during the deployment of sensor nodes. Firstly, by using cellular grids to stack the monitoring area, a bipartite graph model between the nodes and the cellular grids is constructed to minimize and balance the moving distance during the redeployment. Secondly, the vampire bat algorithm is introduced for bipartite graph matching. Finally, the coverage effect and moving distance are further optimized based on the improved virtual force. The simulation results show that the coverage rate is increased by 5.43%, 3.25%, and 1.63%, and the moving distance is reduced by 33.64%, 3.66%, and 2.01% when compared with COSH, VF-IALO, and HA, respectively. In addition, the proposed algorithm shows good performance in terms of uniformity of moving distance and maximum moving distance.

Predicting user-item links in recommender systems based on similarity-network resource allocation

Recommender systems and link prediction techniques have been widely used in areas such as online information filtering and improving user retrieval efficiency, and their performance and principles are of significant research interest. However, existing mainstream recommendation algorithms still face many challenges, such as the contradiction between prediction accuracy and recommendation diversity, and the limited scalability of algorithms due to the need to use a large number of neighbors for prediction. To address these two issues, this paper designs a user-item link prediction algorithm based on resource allocation within the user similarity network to enhance prediction accuracy while maintaining recommendation diversity and using as few neighbors as possible to achieve better algorithm scalability. We first calculate inter-user similarity based on user history ratings and construct a similarity network among users by filtering the similarity results; subsequently, based on the centrality and community features in this network, we design a similarity measure for resource allocation that incorporates the bipartite graph model and the similarity network; finally, we use this similarity method to select the set of prediction target neighbors, synthesize and use the similarity results, centrality, and community features for the prediction of user-item links. Experimental results on two well-known datasets with three state-of-the-art algorithms show that the proposed approach can improve the prediction accuracy by 2.34% to 15.76% in a shorter time and maintain a high recommendation diversity, and the ranking accuracy of recommendation is also improved. Compared with the benchmark algorithm with the second highest performance ranking, the method designed in this paper can further reduce the number of neighbors required at optimal prediction error by 25% to 56%. The study reveals that resource allocation in similarity networks successfully mines the features embedded in the recommender system, laying the foundation for further understanding the recommender system and improving the performance of related prediction methods.

Bipartite Sachdev-Ye-Kitaev model: Conformal limit and level statistics

We study a bipartite version of the Sachdev-Ye-Kitaev (SYK) model. We show that the model remains solvable in the limit of large-$N$ in the same sense as the original model if the ratio of both flavors is kept finite. The scaling dimensions of the two species can be tuned continuously as a function of the ratio. We also investigate the finite-size spectral properties of the model. We show how the level statistics differs from the original SYK model and infer an additional exchange symmetry in the bipartite model.

Quantum Criticality of Antiferromagnetism and Superconductivity with Relativity.

We study a quantum phase transition from a massless to massive Dirac fermion phase in a new two-dimensional bipartite lattice model of electrons that is amenable to sign-free quantum MonteCarlo simulations. Importantly, interactions in our model are not only invariant under SU(2) symmetries of spin and charge like the Hubbard model, but they also preserve an Ising-like electron spin-charge flip symmetry. From unbiased fermion bag MonteCarlo simulations with up to 2304 sites, we show that the massive fermion phase spontaneously breaks this Ising symmetry, picking either antiferromagnetism or superconductivity, and that the transition at which both orders are simultaneously quantum critical belongs to a new "chiral spin-charge symmetric" universality class. We explain our observations using effective potential and renormalization group calculations within the framework of a continuum field theory.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Bipartite network models to design combination therapies in acute myeloid leukaemia

An Advertising Recommendation Algorithm Based on Deep Learning Fusion Model

With the rapid evolution of the Internet and smart mobile devices, personalized advertising is becoming increasingly acceptive on we-media platforms. Traditional advertising push cannot meet users’ demand for personalized advertising, leading to users’ resistance to advertising. Aiming to realize personalized advertising recommendation, an advertising recommendation algorithm based on deep learning fusion model is proposed. The bipartite graph model is applied to network representation learning method to decompose user and advertising content into two networks. The embedded representations of two types of nodes are obtained by training GraphSAGE model on their respective networks. The relation matrix of two kinds of nodes is obtained by using the crossproduct operation. Finally, feature information is extracted by convolutional neural network to achieve personalized advertising recommendation. Experimental results verify the effectiveness of the proposed algorithm, which also achieves good results in accuracy and convergence speed.

Evolving Bipartite Model Reveals the Bounded Weights in Mobile Social Networks

Many realistic mobile social networks can be characterized by evolving bipartite graphs [1], [2], in which dynamically added elements are divided into two entities and connected by links between these two entities, such as users and items in recommendation networks, followees and followers in Twitter networks, authors and scientific topics in scholarly networks, etc. However, given the fact that connections between two entities are often weighted, how to mathematically model such weighted evolving bipartite relationships, along with quantitative characterizations, remains unexplored. Motivated by this, we develop a novel Evolving Bipartite Model (EBM), which, based on empirically validated power-law distribution on multiple realistic mobile social networks, discloses that the distribution of total weights of incoming and outgoing edges in networks is determined by the weighting scale and bounded by certain ceilings and floors. Based on these theoretical results, for evolving bipartite networks whose degree follows power-law distribution, their overall weights of vertices can be predicted by EBM. To illustrate, in recommendation networks, the evaluation of items, i.e., total rating scores, can be estimated through the given bounds; in Twitter social networks, the influence of users can be roughly measured; in scholarly networks

Analysis of an optimal policy in dynamic bipartite matching models

A dynamic bipartite matching model is given by a bipartite matching graph which determines the possible matchings between the various types of supply and demand items. Both supply and demand items arrive to the system according to a stochastic process. Matched pairs leave the system and the others wait in the queues, which induces a holding cost. We model this problem as a Markov Decision Process and study the discounted cost and the average cost problem. We assume that the cost function is linear on the queue sizes. We show that for the N-shaped matching graph, an optimal matching control prioritizes the matchings in the pendant edges and is of threshold type for the diagonal edge. In addition, for the average cost problem, we compute the optimal threshold value. We then show how the obtained results can be used to characterize the structure of an optimal matching control for a quasi-complete graph with an arbitrary number of nodes. For arbitrary bipartite graphs, we show that, when the cost of the pendant edges is larger than in the neighbors, an optimal matching policy prioritizes the items in the pendant edges. We also study the W-shaped matching graph and, when the cost of the pendant edges is larger than the cost of the middle edge, we conjecture that an optimal matching policy is also of threshold type with priority to the pendant edges; however, when the cost of the middle edge is larger, we present simulations that show that it is not optimal to prioritize items in the pendant edges.

Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach

Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual’s age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.