The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynam...The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.展开更多
In the past ten years, community detection in complex networks has attracted more and more attention of researchers. Communities often correspond to functional subunits in the complex systems. In complex network, a no...In the past ten years, community detection in complex networks has attracted more and more attention of researchers. Communities often correspond to functional subunits in the complex systems. In complex network, a node community can be defined as a subgraph induced by a set of nodes, while a link community is a subgraph induced by a set of links. Although most researches pay more attention to identifying node communities in both unipartite and bipartite networks, some researchers have investigated the link community detection problem in unipartite networks. But current research pays little attention to the link community detection problem in bipartite networks. In this paper, we investigate the link community detection problem in bipartite networks, and formulate it into an integer programming model. We proposed a genetic algorithm for partition the bipartite network into overlapping link communities. Simulations are done on both artificial networks and real-world networks. The results show that the bipartite network can be efficiently partitioned into overlapping link communities by the genetic algorithm.展开更多
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 heu...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.展开更多
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...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.展开更多
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： ...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.展开更多
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 ...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.展开更多
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...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.展开更多
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 de...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.展开更多
We compare and contrast asymmetry and nestedness, two concepts used in the characterisation of the specialist-generalist balance in bipartite ecological interaction networks. Our analysis is relevant to mutualistic ne...We compare and contrast asymmetry and nestedness, two concepts used in the characterisation of the specialist-generalist balance in bipartite ecological interaction networks. Our analysis is relevant to mutualistic networks such as those consisting of flowering plants and pollinators, or fruiting plants and frugivores, or antagonistic networks such as those consisting of plants and herbivores, in an ecological community. We shall refer to the two sets of species in the bipartite network as plants and animals, the usual but not the only ecological situation. By asymmetry we mean either connectivity asymmetry or dependence asymmetry, which are essentially equivalent. Asymmetry expresses two attributes: generalists interact preferentially with specialists, and specialists avoid interacting with each other. Nested patterns, in principle, should express these same two features and one more: the presence of a core of interactions among generalists. We compute the full set of perfectly nested patterns that are possible in an L × L matrix with N interactions representing an ecological network of L plants and L animals, and point out that the number of nested arrangements grows exponentially with N. In addition, we analyse asymmetry for the full set of perfectly nested patterns, and identify extremes of asymmetry inside the universe of nested patterns. The minimal asymmetry is marked by a modular core of interactions between species that are neither specialists nor generalists. On the other hand, the case of maximal asymmetry is formed by a set of few generalists and many specialists with equal connectivity. The stereotypic case of nestedness with a core of interactions among generalists has intermediate asymmetry.展开更多
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...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.展开更多
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 ...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.展开更多
高价值专利识别是抢占产业全球战略高地、推动产业持续高效健康发展的重要课题,可为产业关键核心技术的挖掘提供重要线索。本文从专利技术距离测度视角出发,在进行主题聚类提取领域上位类主题基础上,提出一种基于主题知识贡献距离与主...高价值专利识别是抢占产业全球战略高地、推动产业持续高效健康发展的重要课题,可为产业关键核心技术的挖掘提供重要线索。本文从专利技术距离测度视角出发,在进行主题聚类提取领域上位类主题基础上,提出一种基于主题知识贡献距离与主题联系程度双维影响下的高价值专利识别方法。在主题知识贡献距离维度上,构建专利间分层专利引用网络,计算各专利与主题的持续知识贡献值,基于知识贡献时间序列计算主题间的动态时间规整(dynamic time warping,DTW)距离,形成主题知识贡献距离矩阵;在主题联系程度维度上,构建主题与专利二分图网络,结合专利共现频率与引用关系强度进行初始强度与全局逻辑计算,形成主题联系程度矩阵。融合双维度矩阵构建专利技术距离矩阵,基于技术距离矩阵进行各专利的绝对技术距离计算,选取阈值范围内的高绝对技术距离专利作为领域内高技术价值的专利。经验证数据集检验,本文方法的精准率达到0.8218,F1指标达到0.8014。基于此,对“生成式人工智能”领域专利进行实证,识别出产业内具有较高价值的专利1437件,并发现识别出的高价值专利集具有较高的转让比例,转让比例达58.59%。本文基于技术本质的视角对专利间的技术差距进行量化,打破了以往仅从外部特征或简单统计数据判断专利价值的局限性,提升了识别的准确性;同时,提出双维度的技术距离影响机理,进一步提升了识别的可解释性。展开更多
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 ...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.展开更多
基金National Natural Science Foundation of China(No.12071370)。
文摘The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.
文摘In the past ten years, community detection in complex networks has attracted more and more attention of researchers. Communities often correspond to functional subunits in the complex systems. In complex network, a node community can be defined as a subgraph induced by a set of nodes, while a link community is a subgraph induced by a set of links. Although most researches pay more attention to identifying node communities in both unipartite and bipartite networks, some researchers have investigated the link community detection problem in unipartite networks. But current research pays little attention to the link community detection problem in bipartite networks. In this paper, we investigate the link community detection problem in bipartite networks, and formulate it into an integer programming model. We proposed a genetic algorithm for partition the bipartite network into overlapping link communities. Simulations are done on both artificial networks and real-world networks. The results show that the bipartite network can be efficiently partitioned into overlapping link communities by the genetic algorithm.
基金Projects(61363037,31071700)supported by the National Natural Science Foundation of ChinaProject(2011GXNSFD018025)supported by the Natural Science Key Foundation of Guangxi Province,ChinaProject(KYTZ201108)supported by the Development Foundation of Chengdu University of Information Technology,China
文摘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.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11505114 and 10975099)the Program for Professor of Special Appointment(Orientational Scholar)at Shanghai Institutions of Higher Learning(Grant Nos.QD02015016 and DUSST02)+1 种基金the Shanghai Project for Construction of Discipline Peaks,the Natural Science Foundation of Guangxi Zhuang Guangxi Zhuang Autonomous Region(Grant No.2016GXNSFDA380031)the Fundamental Ability Enhancement Project for Young and Middle-aged University Teachers in Guangxi Zhuang Autonomous Region(Grant No.2017KY0859)
文摘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.
文摘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.
基金Project funded by the National Science Foundation of China under Grant(Nos.61462091,61672020,U1803263,61866039,61662085)by the Data Driven Software Engineering innovation team of Yunnan province(No.2017HC012)+2 种基金by Scientific Research Foundation Project of Yunnan Education Department(No.2019J0008,2019J0010)by China Postdoctoral Science Foundation(Nos.2013M542560,2015T81129)A Project of Shandong Province Higher Educational Science and Technology Program(No.J16LN61).
文摘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.
文摘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.
基金Supported by the National Natural Science Foundation of China under Grant No.11271006Shandong Provincial Natural Science Foundation under Grant No.ZR2012GQ002
文摘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.
文摘We compare and contrast asymmetry and nestedness, two concepts used in the characterisation of the specialist-generalist balance in bipartite ecological interaction networks. Our analysis is relevant to mutualistic networks such as those consisting of flowering plants and pollinators, or fruiting plants and frugivores, or antagonistic networks such as those consisting of plants and herbivores, in an ecological community. We shall refer to the two sets of species in the bipartite network as plants and animals, the usual but not the only ecological situation. By asymmetry we mean either connectivity asymmetry or dependence asymmetry, which are essentially equivalent. Asymmetry expresses two attributes: generalists interact preferentially with specialists, and specialists avoid interacting with each other. Nested patterns, in principle, should express these same two features and one more: the presence of a core of interactions among generalists. We compute the full set of perfectly nested patterns that are possible in an L × L matrix with N interactions representing an ecological network of L plants and L animals, and point out that the number of nested arrangements grows exponentially with N. In addition, we analyse asymmetry for the full set of perfectly nested patterns, and identify extremes of asymmetry inside the universe of nested patterns. The minimal asymmetry is marked by a modular core of interactions between species that are neither specialists nor generalists. On the other hand, the case of maximal asymmetry is formed by a set of few generalists and many specialists with equal connectivity. The stereotypic case of nestedness with a core of interactions among generalists has intermediate asymmetry.
文摘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.
文摘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.
文摘高价值专利识别是抢占产业全球战略高地、推动产业持续高效健康发展的重要课题,可为产业关键核心技术的挖掘提供重要线索。本文从专利技术距离测度视角出发,在进行主题聚类提取领域上位类主题基础上,提出一种基于主题知识贡献距离与主题联系程度双维影响下的高价值专利识别方法。在主题知识贡献距离维度上,构建专利间分层专利引用网络,计算各专利与主题的持续知识贡献值,基于知识贡献时间序列计算主题间的动态时间规整(dynamic time warping,DTW)距离,形成主题知识贡献距离矩阵;在主题联系程度维度上,构建主题与专利二分图网络,结合专利共现频率与引用关系强度进行初始强度与全局逻辑计算,形成主题联系程度矩阵。融合双维度矩阵构建专利技术距离矩阵,基于技术距离矩阵进行各专利的绝对技术距离计算,选取阈值范围内的高绝对技术距离专利作为领域内高技术价值的专利。经验证数据集检验,本文方法的精准率达到0.8218,F1指标达到0.8014。基于此,对“生成式人工智能”领域专利进行实证,识别出产业内具有较高价值的专利1437件,并发现识别出的高价值专利集具有较高的转让比例,转让比例达58.59%。本文基于技术本质的视角对专利间的技术差距进行量化,打破了以往仅从外部特征或简单统计数据判断专利价值的局限性,提升了识别的准确性;同时,提出双维度的技术距离影响机理,进一步提升了识别的可解释性。
基金supported by the National Natural Science Foundation of China(Grant Nos.61571414 and 61471331)
文摘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.
