Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolvi...Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.展开更多
Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well a...Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well as to determine the number of communities. In this paper, we proposed the detecting overlapping communities based on vital nodes algorithm（DOCBVA）, an algorithm based on vital nodes and initial seeds to detect overlapping communities. First, through some screening method, we find the vital nodes and then the seed communities through the pretreatment of vital nodes. This process differs from most existing methods, and the speed is faster. Then the seeds will be extended. We also adopt a new parameter of attribution degree to extend the seeds and find the overlapping communities. Finally, the remaining nodes that have not been processed in the first two steps will be reprocessed. The number of communities is likely to change until the end of algorithm. The experimental results using some real-world network data and artificial network data are satisfactory and can prove the superiority of the DOCBVA algorithm.展开更多
Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static com...Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, LabelRankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping communities when compared to other community detection algorithms. SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network.展开更多
Community detection is an important methodology for understanding the intrinsic structure and function of a realworld network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Prop...Community detection is an important methodology for understanding the intrinsic structure and function of a realworld network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm（Abbreviated as DLPA）, to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.展开更多
There are currently many approaches to identify the community structure of a network, but relatively few specific to detect overlapping community structures. Likewise, there are few networks with ground truth overlapp...There are currently many approaches to identify the community structure of a network, but relatively few specific to detect overlapping community structures. Likewise, there are few networks with ground truth overlapping nodes. For this reason,we introduce a new network, Pilgrim, with known overlapping nodes, and a new genetic algorithm for detecting such nodes. Pilgrim is comprised of a variety of structures including two communities with dense overlap,which is common in real social structures. This study initially explores the potential of the community detection algorithm LabelRank for consistent overlap detection;however, the deterministic nature of this algorithm restricts it to very few candidate solutions. Therefore, we propose a genetic algorithm using a restricted edge-based clustering technique to detect overlapping communities by maximizing an efficient overlapping modularity function. The proposed restriction to the edge-based representation precludes the possibility of disjoint communities, thereby, dramatically reducing the search space and decreasing the number of generations required to produce an optimal solution. A tunable parameterr allows the strictness of the definition of overlap to be adjusted allowing for refinement in the number of identified overlapping nodes. Our method, tested on several real social networks, yields results comparable to the most effective overlapping community detection algorithms to date.展开更多
Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysi...Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysis of other scientific networks.Design/methodology/approach: The Q-value variance is defined to achieve overlapping structures of different levels in the scientific networks. At the same time, analyses for time correlation variance and subject correlation variance are used to present the formation of overlapping structures in scientific networks. As a test, a co-citation network of highly cited papers on Molecular Biology & Genetics from Essential Science Indicator(ESI) is taken as an example for an empirical analysis.Findings: Our research showed that the Q-value variance is effective for achieving the desired overlapping structures. Meanwhile, the time correlation variance and subject correlation variance are equally useful for uncovering the evolution progress of scientific research, and the properties of overlapping structures in the research of co-citation network as well.Research limitations: In this paper, the theoretical analysis and verification of time and subject correlation variances are still at its initial stage. Further studies in this regard need to take actual evolution of research areas into consideration.Practical implications: Evolution properties of overlapping structures pave the way for overlapping and evolution analysis of disciplines or areas, this study is of practical value for the planning of scientific and technical innovation.Originality/value: This paper proposes an analytical method of time correlation variance and subject correlation variance based on the evolution properties of overlapping structures, which would provide the foundation for the evolution analysis of disciplines and interdisciplinary research.展开更多
Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is base...Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.展开更多
Community discovery of complex networks,esp.of social networks,has been a hotly debated topic in academic circles in recent years.Since actual networks usually contain some overlapping nodes that are difficult to assi...Community discovery of complex networks,esp.of social networks,has been a hotly debated topic in academic circles in recent years.Since actual networks usually contain some overlapping nodes that are difficult to assign to a certain community,overlapping community discovery is under great demand in practical applications.However,at present network community discovery is mainly done by non-overlapping community discovery methods,overlapping discovery methods are not common.In this context,an overlapping community discovery method is proposed hereby based on topological potential and specific algorithms are also provided.This method not only considers the spread of the uncertainty of community identity of the overlapping nodes in the network,but also realizes a quantified representation,i.e.,uncertainty measure,of the community identity of the overlapping nodes.The experiment results show that this method yields the results that are consistent with those by the classic methods and are more reasonable.展开更多
In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglo...In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglomerating nodes into local communities,and the overlapping nodes are detected based on the idea of making each community strong.We propose a contribution coefficient bvcito measure the contribution of an overlapping node to each of its belonging communities,and the fuzzy coefficients of the overlapping node can be obtained by normalizing the bvci to all its belonging communities.The running time of our method is analyzed and varies linearly with network size.We investigate our method on the computergenerated networks and real networks.The testing results indicate that the accuracy of our method in detecting disjoint communities is higher than those of the existing local methods and our method is efficient for detecting the overlapping nodes with fuzzy coefficients.Furthermore,the local optimizing scheme used in our method allows us to partly solve the resolution problem of the global modularity.展开更多
The studies show that numerous complex networks have clustering effect.It is an indispensable step to identify node clusters in network,namely community,in which nodes are closely related,and in many applications such...The studies show that numerous complex networks have clustering effect.It is an indispensable step to identify node clusters in network,namely community,in which nodes are closely related,and in many applications such as identification of ringleaders in anti-criminal and anti-terrorist network,efficient storage of data in Wireless Sensor Network(WSN).At present,most of community identification methods still require the specifications of the number or the scale of community by user and still can not handle overlapping nodes.In an attempt to solve these problems,a network community identification method based on utility value is proposed,which is a function of each node's clustering coefficient and degree.This method makes use of individual-centered theory for reference and can automatically determine the number of communities.In addition,this method is an overlapping community identification method in nature.It is shown through contrastive experiments that this method is more efficient than other methods based on individual-centered theory when they control the same amount of information.Finally,a research direction is proposed for network community identification method based on the individual-centered theory.展开更多
Predictive modelling for quality analysis becomes one of the most critical requirements for a continuous improvement of reliability, efficiency and safety of laser welding process. Accurate and effective model to perf...Predictive modelling for quality analysis becomes one of the most critical requirements for a continuous improvement of reliability, efficiency and safety of laser welding process. Accurate and effective model to perform non-destructive quality estimation is an essential part of this assessment. This paper presents a structured approach developed to design an effective artificial neural network based model for predicting the weld bead dimensional characteristic in laser overlap welding of low carbon galvanized steel. The modelling approach is based on the analysis of direct and interaction effects of laser welding parameters such as laser power, welding speed, laser beam diameter and gap on weld bead dimensional characteristics such as depth of penetration, width at top surface and width at interface. The data used in this analysis was derived from structured experimental investigations according to Taguchi method and exhaustive FEM based 3D modelling and simulation efforts. Using a factorial design, different neural network based prediction models were developed, implemented and evaluated. The models were trained and tested using experimental data, supported with the data generated by the 3D simulation. Hold-out test and k-fold cross validation combined to various statistical tools were used to evaluate the influence of the laser welding parameters on the performances of the models. The results demonstrated that the proposed approach resulted successfully in a consistent model providing accurate and reliable predictions of weld bead dimensional characteristics under variable welding conditions. The best model presents prediction errors lower than 7% for the three weld quality characteristics.展开更多
基金supported by the National Natural Science Foundation of China(615730176140149961174162)
文摘Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61672124,61370145,61173183,and 61503375)the Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund,China(Grant No.MMJJ20170203)
文摘Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well as to determine the number of communities. In this paper, we proposed the detecting overlapping communities based on vital nodes algorithm（DOCBVA）, an algorithm based on vital nodes and initial seeds to detect overlapping communities. First, through some screening method, we find the vital nodes and then the seed communities through the pretreatment of vital nodes. This process differs from most existing methods, and the speed is faster. Then the seeds will be extended. We also adopt a new parameter of attribution degree to extend the seeds and find the overlapping communities. Finally, the remaining nodes that have not been processed in the first two steps will be reprocessed. The number of communities is likely to change until the end of algorithm. The experimental results using some real-world network data and artificial network data are satisfactory and can prove the superiority of the DOCBVA algorithm.
文摘Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, LabelRankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping communities when compared to other community detection algorithms. SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61173093 and 61202182)the Postdoctoral Science Foundation of China(Grant No.2012 M521776)+2 种基金the Fundamental Research Funds for the Central Universities of Chinathe Postdoctoral Science Foundation of Shannxi Province,Chinathe Natural Science Basic Research Plan of Shaanxi Province,China(Grant Nos.2013JM8019 and 2014JQ8359)
文摘Community detection is an important methodology for understanding the intrinsic structure and function of a realworld network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm（Abbreviated as DLPA）, to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.
文摘There are currently many approaches to identify the community structure of a network, but relatively few specific to detect overlapping community structures. Likewise, there are few networks with ground truth overlapping nodes. For this reason,we introduce a new network, Pilgrim, with known overlapping nodes, and a new genetic algorithm for detecting such nodes. Pilgrim is comprised of a variety of structures including two communities with dense overlap,which is common in real social structures. This study initially explores the potential of the community detection algorithm LabelRank for consistent overlap detection;however, the deterministic nature of this algorithm restricts it to very few candidate solutions. Therefore, we propose a genetic algorithm using a restricted edge-based clustering technique to detect overlapping communities by maximizing an efficient overlapping modularity function. The proposed restriction to the edge-based representation precludes the possibility of disjoint communities, thereby, dramatically reducing the search space and decreasing the number of generations required to produce an optimal solution. A tunable parameterr allows the strictness of the definition of overlap to be adjusted allowing for refinement in the number of identified overlapping nodes. Our method, tested on several real social networks, yields results comparable to the most effective overlapping community detection algorithms to date.
基金supported by the National Science Library of Chinese Academy of Sciences
文摘Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysis of other scientific networks.Design/methodology/approach: The Q-value variance is defined to achieve overlapping structures of different levels in the scientific networks. At the same time, analyses for time correlation variance and subject correlation variance are used to present the formation of overlapping structures in scientific networks. As a test, a co-citation network of highly cited papers on Molecular Biology & Genetics from Essential Science Indicator(ESI) is taken as an example for an empirical analysis.Findings: Our research showed that the Q-value variance is effective for achieving the desired overlapping structures. Meanwhile, the time correlation variance and subject correlation variance are equally useful for uncovering the evolution progress of scientific research, and the properties of overlapping structures in the research of co-citation network as well.Research limitations: In this paper, the theoretical analysis and verification of time and subject correlation variances are still at its initial stage. Further studies in this regard need to take actual evolution of research areas into consideration.Practical implications: Evolution properties of overlapping structures pave the way for overlapping and evolution analysis of disciplines or areas, this study is of practical value for the planning of scientific and technical innovation.Originality/value: This paper proposes an analytical method of time correlation variance and subject correlation variance based on the evolution properties of overlapping structures, which would provide the foundation for the evolution analysis of disciplines and interdisciplinary research.
基金supported by the National Nature Science Foundation of China (61170169, 61170168)
文摘Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61073041,60873037,61100008 and 61073043)the Natural Science Foundation of Heilongjiang Province(Grant No.F200901 and F201023)+1 种基金the Harbin Special Funds for Technological Innovation Research(Grant No. 2010RFXXG002 and 2011RFXXG015)the Fundamental Research Funds for the Central Universities of China(Grant No.HEUCF100602)
文摘Community discovery of complex networks,esp.of social networks,has been a hotly debated topic in academic circles in recent years.Since actual networks usually contain some overlapping nodes that are difficult to assign to a certain community,overlapping community discovery is under great demand in practical applications.However,at present network community discovery is mainly done by non-overlapping community discovery methods,overlapping discovery methods are not common.In this context,an overlapping community discovery method is proposed hereby based on topological potential and specific algorithms are also provided.This method not only considers the spread of the uncertainty of community identity of the overlapping nodes in the network,but also realizes a quantified representation,i.e.,uncertainty measure,of the community identity of the overlapping nodes.The experiment results show that this method yields the results that are consistent with those by the classic methods and are more reasonable.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51278101 and 51578149)the Science and Technology Program of Ministry of Transport of China(Grant No.2015318J33080)+1 种基金the Jiangsu Provincial Post-doctoral Science Foundation,China(Grant No.1501046B)the Fundamental Research Funds for the Central Universities,China(Grant No.Y0201500219)
文摘In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglomerating nodes into local communities,and the overlapping nodes are detected based on the idea of making each community strong.We propose a contribution coefficient bvcito measure the contribution of an overlapping node to each of its belonging communities,and the fuzzy coefficients of the overlapping node can be obtained by normalizing the bvci to all its belonging communities.The running time of our method is analyzed and varies linearly with network size.We investigate our method on the computergenerated networks and real networks.The testing results indicate that the accuracy of our method in detecting disjoint communities is higher than those of the existing local methods and our method is efficient for detecting the overlapping nodes with fuzzy coefficients.Furthermore,the local optimizing scheme used in our method allows us to partly solve the resolution problem of the global modularity.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 61073041,60873037,61100008 and 61073043)the Natural Science Foundation of Heilongjiang Province (Grant No. F200901 and F201023 )+1 种基金the Harbin Special Funds for Technological Innovation Research (Grant No.2010RFXXG002 and 2011RFXXG015)the Fundamental Research Funds for the Central Universities of China(Grant No. HEUCF100602)
文摘The studies show that numerous complex networks have clustering effect.It is an indispensable step to identify node clusters in network,namely community,in which nodes are closely related,and in many applications such as identification of ringleaders in anti-criminal and anti-terrorist network,efficient storage of data in Wireless Sensor Network(WSN).At present,most of community identification methods still require the specifications of the number or the scale of community by user and still can not handle overlapping nodes.In an attempt to solve these problems,a network community identification method based on utility value is proposed,which is a function of each node's clustering coefficient and degree.This method makes use of individual-centered theory for reference and can automatically determine the number of communities.In addition,this method is an overlapping community identification method in nature.It is shown through contrastive experiments that this method is more efficient than other methods based on individual-centered theory when they control the same amount of information.Finally,a research direction is proposed for network community identification method based on the individual-centered theory.
文摘Predictive modelling for quality analysis becomes one of the most critical requirements for a continuous improvement of reliability, efficiency and safety of laser welding process. Accurate and effective model to perform non-destructive quality estimation is an essential part of this assessment. This paper presents a structured approach developed to design an effective artificial neural network based model for predicting the weld bead dimensional characteristic in laser overlap welding of low carbon galvanized steel. The modelling approach is based on the analysis of direct and interaction effects of laser welding parameters such as laser power, welding speed, laser beam diameter and gap on weld bead dimensional characteristics such as depth of penetration, width at top surface and width at interface. The data used in this analysis was derived from structured experimental investigations according to Taguchi method and exhaustive FEM based 3D modelling and simulation efforts. Using a factorial design, different neural network based prediction models were developed, implemented and evaluated. The models were trained and tested using experimental data, supported with the data generated by the 3D simulation. Hold-out test and k-fold cross validation combined to various statistical tools were used to evaluate the influence of the laser welding parameters on the performances of the models. The results demonstrated that the proposed approach resulted successfully in a consistent model providing accurate and reliable predictions of weld bead dimensional characteristics under variable welding conditions. The best model presents prediction errors lower than 7% for the three weld quality characteristics.
