Artykuły w czasopismach: „Complex Social Networks”

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Gong, Yuhui, i Qian Yu. "Evolution of Conformity Dynamics in Complex Social Networks". Symmetry 11, nr 3 (28.02.2019): 299. http://dx.doi.org/10.3390/sym11030299.

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Conformity is a common phenomenon among people in social networks. In this paper, we focus on customers’ conformity behaviors in a symmetry market where customers are located in a social network. We establish a conformity model and analyze it in ring network, random network, small-world network, and scale-free network. Our simulations shown that topology structure, network size, and initial market share have significant effects on the evolution of customers’ conformity behaviors. The market will likely converge to a monopoly state in small-world networks but will form a duopoly market in scale networks. As the size of the network increases, there is a greater possibility of forming a dominant group of preferences in small-world network, and the market will converge to the monopoly of the product which has the initial selector in the market. Also, network density will become gradually significant in small-world networks.

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Xuan, Qi, Zhi-Yuan Zhang, Chenbo Fu, Hong-Xiang Hu i Vladimir Filkov. "Social Synchrony on Complex Networks". IEEE Transactions on Cybernetics 48, nr 5 (maj 2018): 1420–31. http://dx.doi.org/10.1109/tcyb.2017.2696998.

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CENTOLA, DAMON. "Failure in Complex Social Networks". Journal of Mathematical Sociology 33, nr 1 (30.12.2008): 64–68. http://dx.doi.org/10.1080/00222500802536988.

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López-Pintado, Dunia. "Diffusion in complex social networks". Games and Economic Behavior 62, nr 2 (marzec 2008): 573–90. http://dx.doi.org/10.1016/j.geb.2007.08.001.

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Liu, Guanfeng, Yan Wang i Mehmet Orgun. "Optimal Social Trust Path Selection in Complex Social Networks". Proceedings of the AAAI Conference on Artificial Intelligence 24, nr 1 (5.07.2010): 1391–98. http://dx.doi.org/10.1609/aaai.v24i1.7509.

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Online social networks are becoming increasingly popular and are being used as the means for a variety of rich activities. This demands the evaluation of the trustworthiness between two unknown participants along a certain social trust path between them in the social network. However, there are usually many social trust paths between participants. Thus, a challenging problem is finding which social trust path is the optimal one that can yield the most trustworthy evaluation result.In this paper, we first present a new complex social network structure and a new concept of Quality of Trust (QoT) to illustrate the ability to guarantee a certain level of trustworthiness in trust evaluation. We then model the optimal social trust path selection as a Multi-Constrained Optimal Path (MCOP) selection problem which is NP-Complete. For solving this problem, we propose an efficient approximation algorithm MONTE K based on the Monte Carlo method. The results of our experiments conducted on a real dataset of social networks illustrate that our proposed algorithm significantly outperforms existing approaches in both efficiency and the quality of selected social trust paths.

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Liu, Guanfeng, Yan Wang i Mehmet Orgun. "Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks". Proceedings of the AAAI Conference on Artificial Intelligence 26, nr 1 (20.09.2021): 101–7. http://dx.doi.org/10.1609/aaai.v26i1.8114.

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Trust is one of the most important factors for participants' decision-making in Online Social Networks (OSNs). The trust network from a source to a target without any prior interaction contains some important intermediate participants, the trust relations between the participants, and the social context, each of which has an important influence on trust evaluation. Thus, before performing any trust evaluation, the contextual trust network from a given source to a target needs to be extracted first, where constraints on the social context should also be considered to guarantee the quality of extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first propose a complex contextual social network structure which considers social contextual impact factors. These factors have significant influences on both social interaction between participants and trust evaluation. Then, we propose a new concept called QoTN (Quality of Trust Network) and a social context-aware trust network discovery model. Finally, we propose a Social Context-Aware trust Network discovery algorithm (SCAN) by adopting the Monte Carlo method and our proposed optimization strategies. The experimental results illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust network.

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Asbaş, Caner, Zühal Şenyuva i Şule Tuzlukaya. "New Organizations in Complex Networks: Survival and Success". Central European Management Journal 30, nr 1 (15.03.2022): 11–39. http://dx.doi.org/10.7206/cemj.2658-0845.68.

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Purpose: The present study investigates the survival and success of new organizations in the light of complex network theory. Methodology: The empirical data was collected using the survey method from the technology park companies are analyzed with social network analysis. Two main methods were used in this study: descriptive statistics and social network analysis. Findings: The findings indicate that new nodes appearing because of splitting up of bigger nodes from present or other related networks have a higher degree of centrality. In practice, this means that companies founded by former members of large-scale companies from these networks are more successful due to the ease in providing the flow of resources and information through previous links. This suggests that the imprint effect can be observed in the appearance, lifecycle, and performance of new nodes in complex networks. Originality: The literature lacks studies on new organizations’ lifecycle in complex networks despite the existence of studies about new organizations in organizational networks. This study examines the appearance, success, and survival of new organizations in networks by complex network approaches such as dynamism, dissipative structures, and uncertainties.

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Yeqing, Zhao. "Knowledge Evolution of Complex Agent Networks". MATEC Web of Conferences 173 (2018): 03050. http://dx.doi.org/10.1051/matecconf/201817303050.

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In order to study the interaction between structure change of social network and knowledge propagation, this paper proposes a complex agent network model to discover the inner rule and restraining factors of the knowledge diffusion in the network system. The agents take advantage of different social radius to form acquaintance networks based on the theory of social circles in the knowledge propagation network model, and the dynamic evolution process of knowledge network is realized according the defined rules of knowledge communication. Simulation results show that this model based on social circle theory can better realize the characteristics of the actual social network than the traditional network model established before, at the same time the social radius of knowledge agent for knowledge dissemination in knowledge network has the obvious effect. It can narrow the knowledge gap for the knowledge agents in social network and good social relation network can be developed.

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Yeqing, Zhao. "Knowledge Evolution of Complex Agent Networks". MATEC Web of Conferences 176 (2018): 03007. http://dx.doi.org/10.1051/matecconf/201817603007.

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In order to study the interaction between structure change of social network and knowledge propagation, this paper proposes a complex agent network model to discover the inner rule and restraining factors of the knowledge diffusion in the network system. The agents take advantage of different social radius to form acquaintance networks based on the theory of social circles in the knowledge propagation network model, and the dynamic evolution process of knowledge network is realized according the defined rules of knowledge communication. Simulation results show that this model based on social circle theory can better realize the characteristics of the actual social network than the traditional network model established before, at the same time the social radius of knowledge agent for knowledge dissemination in knowledge network has the obvious effect. It can narrow the knowledge gap for the knowledge agents in social network and good social relation network can be developed.

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Liu, Guanfeng, Yan Wang i Mehmet Orgun. "Trust Transitivity in Complex Social Networks". Proceedings of the AAAI Conference on Artificial Intelligence 25, nr 1 (4.08.2011): 1222–29. http://dx.doi.org/10.1609/aaai.v25i1.8087.

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In Online Social Networks (OSNs), participants can conduct rich activities, where trust is one of the most important factors for their decision making. This necessitates the evaluation of the trustworthiness between two unknown participants along the social trust paths between them based on the trust transitivity properties (i.e., if A trusts B and B trusts C, then A can trust C to some extent). In order to compute more reasonable trust value between two unknown participants, a critical and challenging problem is to make clear how and to what extent trust is transitive along a social trust path. To address this problem, we first propose a new complex social network structure that takes, besides trust, social relationships, recommendation roles and preference similarity between participants into account. These factors have significant influence on trust transitivity. We then propose a general concept, called Quality of Trust Transitivity (QoTT), that takes any factor with impact on trust transitivity as an attribute to illustrate the ability of a trust path to guarantee a certain level of quality in trust transitivity. Finally, we propose a novel Multiple QoTT Constrained Trust Transitivity (MQCTT) model. The results of our experiments demonstrate that our proposed MQCTT model follows the properties of trust and the principles illustrated in social psychology, and thus can compute more resonable trust values than existing methods that consider neither the impact of social aspects nor the properties of trust.

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Herrera, Jose L., Ravi Srinivasan, John S. Brownstein, Alison P. Galvani i Lauren Ancel Meyers. "Disease Surveillance on Complex Social Networks". PLOS Computational Biology 12, nr 7 (14.07.2016): e1004928. http://dx.doi.org/10.1371/journal.pcbi.1004928.

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Mao, Fubing, Lijia Ma, Qiang He i Gaoxi Xiao. "Match making in complex social networks". Applied Mathematics and Computation 371 (kwiecień 2020): 124928. http://dx.doi.org/10.1016/j.amc.2019.124928.

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Yang, Weiyu, Jia Wu i Jingwen Luo. "Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks". Complexity 2020 (8.06.2020): 1–20. http://dx.doi.org/10.1155/2020/3721579.

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In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency.

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Smailovic, Vanja, Vedran Podobnik i Ignac Lovrek. "A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks". Complexity 2018 (8.08.2018): 1–20. http://dx.doi.org/10.1155/2018/1084795.

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Online social networks are complex systems often involving millions or even billions of users. Understanding the dynamics of a social network requires analysing characteristics of the network (in its entirety) and the users (as individuals). This paper focuses on calculating user’s social influence, which depends on (i) the user’s positioning in the social network and (ii) interactions between the user and all other users in the social network. Given that data on all users in the social network is required to calculate social influence, something not applicable for today’s social networks, alternative approaches relying on a limited set of data on users are necessary. However, these approaches introduce uncertainty in calculating (i.e., predicting) the value of social influence. Hence, a methodology is proposed for evaluating algorithms that calculate social influence in complex social networks; this is done by identifying the most accurate and precise algorithm. The proposed methodology extends the traditional ground truth approach, often used in descriptive statistics and machine learning. Use of the proposed methodology is demonstrated using a case study incorporating four algorithms for calculating a user’s social influence.

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Ling, Bingo Wing-Kuen, Charlotte Yuk-Fan Ho, Lidong Wang, Kok-Lay Teo, Chi K. Tse i Qingyun Dai. "Near consensus complex linear and nonlinear social networks". Modern Physics Letters B 28, nr 13 (30.05.2014): 1450106. http://dx.doi.org/10.1142/s0217984914501061.

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Some of the nodes of complex social networks may support for a given proposal, while the rest of the nodes may be against the given proposal. Even though all the nodes support for or are against the given proposal, the decision certitudes of individual nodes may be different. In this case, the steady state values of the decision certitudes of the majority of the nodes are either higher than or lower than a threshold value. Deriving the near consensus property is a key to the analysis of the behaviors of complex social networks. So far, no result on the behaviors of the complex social networks satisfying the near consensus property has been reported. Hence, it is useful to extend the definition of the exact consensus property to that of a near consensus property and investigate the behaviors of the complex social networks satisfying the near consensus property. This paper extends the definition of exact consensus complex social networks to that of near consensus complex social networks. For complex linear social networks, this paper investigates the relationships among the vectors representing the steady state values of the decision certitudes of the nodes, the influence weight matrix and the set of vectors representing the initial state values of the decision certitudes of the nodes under a given near consensus specification. The above analysis is based on the Eigen theory. For complex nonlinear social networks with certain types of nonlinearities, the relationship between the influence weight matrix and the vectors representing the steady state values of the decision certitudes of the nodes is studied. When a complex nonlinear social network does not achieve the exact consensus property, the optimal near consensus condition that the complex social network can achieve is derived. This problem is formulated as an optimization problem. The total number of nodes that the decision certitudes of the nodes are either higher than or lower than a threshold value is maximized subject to the corresponding near consensus specification. The optimization problem is a nonsmooth optimization problem. The nonsmooth constraints are first approximated by smooth constraints. Then, the approximated optimization problem is solved via a conventional smooth optimization approach. Computer numerical simulation results as well as the comparisons of the behaviors of complex nonlinear social networks to those of the complex linear social networks are presented. The obtained results demonstrate that some complex social networks can satisfy the near consensus property but not the exact consensus property. Also, the conditions for the near consensus property are dependent on the types of nonlinearities, the influence weight matrix and the vectors representing the initial state values of the decision certitudes of the nodes.

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Šubelj, Lovro. "Convex skeletons of complex networks". Journal of The Royal Society Interface 15, nr 145 (sierpień 2018): 20180422. http://dx.doi.org/10.1098/rsif.2018.0422.

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A convex network can be defined as a network such that every connected induced subgraph includes all the shortest paths between its nodes. A fully convex network would therefore be a collection of cliques stitched together in a tree. In this paper, we study the largest high-convexity part of empirical networks obtained by removing the least number of edges, which we call a convex skeleton. A convex skeleton is a generalization of a network spanning tree in which each edge can be replaced by a clique of arbitrary size. We present different approaches for extracting convex skeletons and apply them to social collaboration and protein interactions networks, autonomous systems graphs and food webs. We show that the extracted convex skeletons retain the degree distribution, clustering, connectivity, distances, node position and also community structure, while making the shortest paths between the nodes largely unique. Moreover, in the Slovenian computer scientists coauthorship network, a convex skeleton retains the strongest ties between the authors, differently from a spanning tree or high-betweenness backbone and high-salience skeleton. A convex skeleton thus represents a simple definition of a network backbone with applications in coauthorship and other social collaboration networks.

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Li, Xiang, i Bocheng Hou. "Competing Complex Information Spreading in Multiplex Social Network". Complexity 2021 (11.05.2021): 1–9. http://dx.doi.org/10.1155/2021/9923837.

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Coevolution spreading dynamics on complex networks is a hot topic, which attracts much attention in network science. This paper proposes a mathematical model to describe the two competing complex information spreading dynamics on multiplex networks. An individual can only accept one of the two pieces of information. A heterogeneous mean-field theory is developed to describe the spreading dynamics. We reveal different regions through Monte Carlo simulations of the competing complex information spreading dynamics: no global information, one information dominant, and two information coexistence. We finally find that the heterogeneity of the multiplex networks’ degree distributions does not qualitatively affect the results.

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Small, Michael, Lvlin Hou i Linjun Zhang. "Random complex networks". National Science Review 1, nr 3 (18.07.2014): 357–67. http://dx.doi.org/10.1093/nsr/nwu021.

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Abstract Exactly what is meant by a ‘complex’ network is not clear; however, what is clear is that it is something other than a random graph. Complex networks arise in a wide range of real social, technological and physical systems. In all cases, the most basic categorization of these graphs is their node degree distribution. Particular groups of complex networks may exhibit additional interesting features, including the so-called small-world effect or being scale-free. There are many algorithms with which one may generate networks with particular degree distributions (perhaps the most famous of which is preferential attachment). In this paper, we address what it means to randomly choose a network from the class of networks with a particular degree distribution, and in doing so we show that the networks one gets from the preferential attachment process are actually highly pathological. Certain properties (including robustness and fragility) which have been attributed to the (scale-free) degree distribution are actually more intimately related to the preferential attachment growth mechanism. We focus here on scale-free networks with power-law degree sequences—but our methods and results are perfectly generic.

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Guo, Dong Wei, Xiang Yan Meng i Cai Fang Hou. "Building Complex Network Similar to Facebook". Applied Mechanics and Materials 513-517 (luty 2014): 909–13. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.909.

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Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

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Wickramasinghe, Shandeepa, Onyekachukwu Onyerikwu, Jie Sun i Daniel ben-Avraham. "Modeling Spatial Social Complex Networks for Dynamical Processes". Complexity 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/1428719.

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The study of social networks—where people are located, geographically, and how they might be connected to one another—is a current hot topic of interest, because of its immediate relevance to important applications, from devising efficient immunization techniques for the arrest of epidemics to the design of better transportation and city planning paradigms to the understanding of how rumors and opinions spread and take shape over time. We develop a Spatial Social Complex Network (SSCN) model that captures not only essential connectivity features of real-life social networks, including a heavy-tailed degree distribution and high clustering, but also the spatial location of individuals, reproducing Zipf’s law for the distribution of city populations as well as other observed hallmarks. We then simulate Milgram’s Small-World experiment on our SSCN model, obtaining good qualitative agreement with the known results and shedding light on the role played by various network attributes and the strategies used by the players in the game. This demonstrates the potential of the SSCN model for the simulation and study of the many social processes mentioned above, where both connectivity and geography play a role in the dynamics.

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Tian, Yang, Guoqi Li i Pei Sun. "Information evolution in complex networks". Chaos: An Interdisciplinary Journal of Nonlinear Science 32, nr 7 (lipiec 2022): 073105. http://dx.doi.org/10.1063/5.0096009.

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Many biological phenomena or social events critically depend on how information evolves in complex networks. However, a general theory to characterize information evolution is yet absent. Consequently, numerous unknowns remain about the mechanisms underlying information evolution. Among these unknowns, a fundamental problem, being a seeming paradox, lies in the coexistence of local randomness, manifested as the stochastic distortion of information content during individual–individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. Here, we attempt to formalize information evolution and explain the coexistence of randomness and regularity in complex networks. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but noise accounts for disturbing them. We further demonstrate the ubiquity of our discovered laws by analyzing the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks.

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MARC, TILEN, i LOVRO ŠUBELJ. "Convexity in complex networks". Network Science 6, nr 2 (6.02.2018): 176–203. http://dx.doi.org/10.1017/nws.2017.37.

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AbstractMetric graph properties lie in the heart of the analysis of complex networks, while in this paper we study their convexity through mathematical definition of a convex subgraph. A subgraph is convex if every geodesic path between the nodes of the subgraph lies entirely within the subgraph. According to our perception of convexity, convex network is such in which every connected subset of nodes induces a convex subgraph. We show that convexity is an inherent property of many networks that is not present in a random graph. Most convex are spatial infrastructure networks and social collaboration graphs due to their tree-like or clique-like structure, whereas the food web is the only network studied that is truly non-convex. Core–periphery networks are regionally convex as they can be divided into a non-convex core surrounded by a convex periphery. Random graphs, however, are only locally convex meaning that any connected subgraph of size smaller than the average geodesic distance between the nodes is almost certainly convex. We present different measures of network convexity and discuss its applications in the study of networks.

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Massaro, E., i F. Bagnoli. "Hierarchical Community Structure in Complex (Social) Networks". Acta Physica Polonica B Proceedings Supplement 7, nr 2 (2014): 379. http://dx.doi.org/10.5506/aphyspolbsupp.7.379.

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Acemoglu, Daron, Munther Dahleh, Ilan Lobel i Asuman Ozdaglar. "Learning Over Complex Social Networks [Extended Abstract]". IFAC Proceedings Volumes 42, nr 10 (2009): 770–73. http://dx.doi.org/10.3182/20090706-3-fr-2004.00128.

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NagaiahMaddikayala, Veera, i R. Chandrasekhar. "A Clustering Algorithm in Complex Social Networks". International Journal of Computer Applications 103, nr 4 (18.10.2014): 24–28. http://dx.doi.org/10.5120/18063-8996.

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Benham-Hutchins, Marge, i Thomas R. Clancy. "Social Networks as Embedded Complex Adaptive Systems". JONA: The Journal of Nursing Administration 40, nr 9 (wrzesień 2010): 352–56. http://dx.doi.org/10.1097/nna.0b013e3181ee42bc.

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TOMASSINI, MARCO, ENEA PESTELACCI i LESLIE LUTHI. "SOCIAL DILEMMAS AND COOPERATION IN COMPLEX NETWORKS". International Journal of Modern Physics C 18, nr 07 (lipiec 2007): 1173–85. http://dx.doi.org/10.1142/s0129183107011212.

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In this paper we extend the investigation of cooperation in some classical evolutionary games on populations where the network of interactions among individuals is of the scale-free type. We show that the update rule, the payoff computation and, to some extent the timing of the operations, have a marked influence on the transient dynamics and on the amount of cooperation that can be established at equilibrium. We also study the dynamical behavior of the populations and their evolutionary stability.

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Musial, Katarzyna, Piotr Bródka i Pasquale De Meo. "Analysis and Applications of Complex Social Networks". Complexity 2017 (2017): 1–2. http://dx.doi.org/10.1155/2017/3014163.

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Hasgall, Alon. "Digital social networks as complex adaptive systems". VINE 43, nr 1 (8.02.2013): 78–95. http://dx.doi.org/10.1108/03055721311302151.

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Delgado, Jordi. "Emergence of social conventions in complex networks". Artificial Intelligence 141, nr 1-2 (październik 2002): 171–85. http://dx.doi.org/10.1016/s0004-3702(02)00262-x.

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Tsekeris, Charalambos, i Ioannis Katerelos. "Web 2.0, complex networks and social dynamics". Contemporary Social Science 7, nr 3 (listopad 2012): 233–46. http://dx.doi.org/10.1080/21582041.2012.721896.

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MALETIĆ, SLOBODAN, DANIJELA HORAK i MILAN RAJKOVIĆ. "COOPERATION, CONFLICT AND HIGHER-ORDER STRUCTURES OF SOCIAL NETWORKS". Advances in Complex Systems 15, supp01 (czerwiec 2012): 1250055. http://dx.doi.org/10.1142/s0219525912500555.

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Simplicial complexes represent powerful models of complex networks and complex systems in general. We explore the properties of spectra of combinatorial Laplacian operator of simplicial complexes in the context of connectivity of cliques in the simplicial clique complex associated with social networks. The necessity of higher order spectral analysis is discussed and compared with results for ordinary graph spectra. Methods and results are applied using social network of the Zachary karate club and the network of characters from Victor Hugo's novel Les Miserables.

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Zhao, Qiang, Yue Shen i Chaoqian Li. "Credit Behaviors of Rural Households in the Perspective of Complex Social Networks". Complexity 2021 (4.06.2021): 1–13. http://dx.doi.org/10.1155/2021/9975856.

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With the increasing number of social networks emerging and evolving, the influence of social networks on human behavior is now again a subject of discussion in academe. Dynamics in social networks, such as opinion formation and information sharing, are restricting or proliferating members’ behavior on social networks, while new social network dynamics are created by interpersonal contacts and interactions. Based on this and against the backdrop of unfavourable rural credit development, this article uses CHFS data to discuss the whole and heterogeneous impact of social networks on rural household credit behavior. The results show that (1) social networks can effectively promote rural household credit behavior; (2) social networks have a significant positive impact on both formal credit and informal credit, but the influence of the latter is stronger; (3) both emotional networks and instrumental networks have a positive impact on formal credit and informal credit, and their influences are stronger on informal credit; (4) the influence of emotional network is stronger than instrumental networks on either formal credit or informal credit.

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Akula, Ramya, i Ivan Garibay. "VizTract: Visualization of Complex Social Networks for Easy User Perception". Big Data and Cognitive Computing 3, nr 1 (21.02.2019): 17. http://dx.doi.org/10.3390/bdcc3010017.

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Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a two-level graph abstraction visualization tool that is designed to visualize both hierarchical and adjacency information in a tree structure. We use the Facebook dataset from the Social Network Analysis Project from Stanford University. On this data, social groups are referred as circles, social network users as nodes, and interactions as edges between the nodes. Our approach is to present a visual overview that represents the interactions between circles, then let the user navigate this overview and select the nodes in the circles to obtain more information on demand. VizTract aim to reduce visual clutter without any loss of information during visualization. VizTract enhances the visual perception of complex social networks to help better understand the dynamics of the network structure. VizTract within a single frame not only reduces the complexity but also avoids redundancy of the nodes and the rendering time. The visualization techniques used in VizTract are the force-directed layout, circle packing, cluster dendrogram, and hierarchical edge bundling. Furthermore, to enhance the visual information perception, VizTract provides interaction techniques such as selection, path highlight, mouse-hover, and bundling strength. This method helps social network researchers to display large networks in a visually effective way that is conducive to ease interpretation and analysis. We conduct a study to evaluate the user experience of the system and then collect information about their perception via a survey. The goal of the study is to know how humans can interpret the network when visualized using different visualization methods. Our results indicate that users heavily prefer those visualization techniques that aggregate the information and the connectivity within a given space, such as hierarchical edge bundling.

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Wang, Xue-Guang. "Research on Critical Nodes Algorithm in Social Complex Networks". Open Physics 15, nr 1 (16.03.2017): 68–73. http://dx.doi.org/10.1515/phys-2017-0008.

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AbstractDiscovering critical nodes in social networks has many important applications and has attracted more and more institutions and scholars. How to determine the K critical nodes with the most influence in a social network is a NP (define) problem. Considering the widespread community structure, this paper presents an algorithm for discovering critical nodes based on two information diffusion models and obtains each node’s marginal contribution by using a Monte-Carlo method in social networks. The solution of the critical nodes problem is the K nodes with the highest marginal contributions. The feasibility and effectiveness of our method have been verified on two synthetic datasets and four real datasets.

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Perova, Juю P., V. P. Grigoriev i D. O. Zhukov. "Models and methods for analyzing complex networks and social network structures". Russian Technological Journal 11, nr 2 (7.04.2023): 33–49. http://dx.doi.org/10.32362/2500-316x-2023-11-2-33-49.

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Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (for example, districts, settlements of small towns, etc.) and wider regions.Methods. Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. Numerical modeling methods for analyzing complex networks and processes occurring in them are considered and described in detail. Special attention is paid to data processing in complex network structures using the Python language and its various available libraries.Results. The specifics of the tasks to be solved in the study of complex network structures and their interdisciplinarity associated with the use of methods of system analysis are described in terms of the theory of complex networks, text analytics, and computational linguistics. In particular, the dynamic models of processes observed in complex social network systems, as well as the structural characteristics of such networks and their relationship with the observed dynamic processes including using the theory of constructing dynamic graphs are studied. The use of neural networks to predict the evolution of dynamic processes and structure of complex social systems is investigated. When creating models describing the observed processes, attention is focused on the use of computational linguistics methods to extract knowledge from text messages of users of social networks.Conclusions. Network analysis can be used to structure models of interaction between social units: people, collectives, organizations, etc. Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. Since communication in social networks almost entirely consists of text messages and various publications, almost all relevant studies use textual analysis methods in conjunction with machine learning and artificial intelligence technologies. Of these, convolutional neural networks demonstrated the best results. However, the use of support vector and decision tree methods should also be mentioned, since these contributed considerably to accuracy. In addition, statistical methods are used to compile data samples and analyze obtained results.

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Westaby, James D., i Adam K. Parr. "Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks". Journal of Applied Behavioral Science 56, nr 1 (24.10.2019): 107–29. http://dx.doi.org/10.1177/0021886319881496.

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Grounded in dynamic network theory, this study examined network goal analysis (NGA) to understand complex systems. NGA provides new insights by inserting goal nodes into social networks. Goal nodes can also represent missions, objectives, or desires, thus having wide applicability. The theory ties social networks to goal nodes through a parsimonious set of social network role linkages, such as independent goal striving, system supporting, feedback, goal preventing, supportive resisting, and system negating (i.e., those who are upset with others in the pursuit). Moreover, we extend the theory’s system reactance role linkage to better account for constructive conflicts. Two complex systems were examined: a team’s mission and an individual’s work project. In support of dynamic network theory, using the Quadratic Assignment Procedure, results demonstrated significant shared goal striving, system supporting, and shared connections between goal striving and system supporting. These findings manifest what we coin as multipendence: Systems having some actions independently involved with goals, while others are dependently involved in the associated network. NGA also demonstrated that the goal nodes manifested strong betweenness centrality, indicating that goal striving and feedback links were connecting entities across the wider system. Strategies to plan network goal interventions are illustrated with implications for practice.

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Firth, Josh A., Ben C. Sheldon i Lauren J. N. Brent. "Indirectly connected: simple social differences can explain the causes and apparent consequences of complex social network positions". Proceedings of the Royal Society B: Biological Sciences 284, nr 1867 (15.11.2017): 20171939. http://dx.doi.org/10.1098/rspb.2017.1939.

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Animal societies are often structurally complex. How individuals are positioned within the wider social network (i.e. their indirect social connections) has been shown to be repeatable, heritable and related to key life-history variables. Yet, there remains a general lack of understanding surrounding how complex network positions arise, whether they indicate active multifaceted social decisions by individuals, and how natural selection could act on this variation. We use simulations to assess how variation in simple social association rules between individuals can determine their positions within emerging social networks. Our results show that metrics of individuals' indirect connections can be more strongly related to underlying simple social differences than metrics of their dyadic connections. External influences causing network noise (typical of animal social networks) generally inflated these differences. The findings demonstrate that relationships between complex network positions and other behaviours or fitness components do not provide sufficient evidence for the presence, or importance, of complex social behaviours, even if direct network metrics provide less explanatory power than indirect ones. Interestingly however, a plausible and straightforward heritable basis for complex network positions can arise from simple social differences, which in turn creates potential for selection to act on indirect connections.

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Zhu, Yaling, Yue Shen i Qiang Zhao. "Self-Presentation and Adolescent Altruistic Behaviors in Social Networks". Complexity 2020 (6.08.2020): 1–11. http://dx.doi.org/10.1155/2020/1719564.

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Social networks provide a convenient place for people to interact; members in social networks may create new connections or break existing connections, driving the evolution of complex network structure. Dynamics in social networks, such as opinion formation and spreading dynamics, may result in complex collective phenomena. This paper conducts a survey on 495 students from six schools in Shaanxi, Henan, and Zhejiang provinces and discusses the impact of self-presentation on adolescent network altruistic behaviors, the intermediary role of social ability cognition, and the moderating role of privacy awareness. The results show the following: (1) Self-presentation in social networks can positively predict adolescent network altruistic behaviors. The positive prediction effect of network sharing is the largest, and the positive prediction effect of network support is the least. (2) Social ability cognition plays an intermediary role between self-presentation and adolescent network altruistic behaviors. (3) The moderating effect of privacy awareness is not significant.

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Li, Ning, Qian Huang, Xiaoyu Ge, Miao He, Shuqin Cui, Penglin Huang, Shuairan Li i Sai-Fu Fung. "A Review of the Research Progress of Social Network Structure". Complexity 2021 (7.01.2021): 1–14. http://dx.doi.org/10.1155/2021/6692210.

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Social network theory is an important paradigm of social structure research, which has been widely used in various fields of research. This paper reviews the development process and the latest progress of social network theory research and analyzes the research application of social network. In order to reveal the deep social structure, this paper analyzes the structure of social networks from three levels: microlevel, mesolevel, and macrolevel and reveals the origin, development, perfection, and latest achievements of complex network models. The regular graph model, P1 model, P2 model, exponential random graph model, small-world network model, and scale-free network model are introduced. In the end, the research on the social network structure is reviewed, and social support network and social discussion network are introduced, which are two important contents of social network research. At present, the research on social networks has been widely used in coauthor networks, citation networks, mobile social networks, enterprise knowledge management, and individual happiness, but there are few research studies on multilevel structure, dynamic research, complex network research, whole network research, and discussion network research. This provides space for future research on social networks.

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Li, Ning, Qian Huang, Xiaoyu Ge, Miao He, Shuqin Cui, Penglin Huang, Shuairan Li i Sai-Fu Fung. "A Review of the Research Progress of Social Network Structure". Complexity 2021 (7.01.2021): 1–14. http://dx.doi.org/10.1155/2021/6692210.

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Social network theory is an important paradigm of social structure research, which has been widely used in various fields of research. This paper reviews the development process and the latest progress of social network theory research and analyzes the research application of social network. In order to reveal the deep social structure, this paper analyzes the structure of social networks from three levels: microlevel, mesolevel, and macrolevel and reveals the origin, development, perfection, and latest achievements of complex network models. The regular graph model, P1 model, P2 model, exponential random graph model, small-world network model, and scale-free network model are introduced. In the end, the research on the social network structure is reviewed, and social support network and social discussion network are introduced, which are two important contents of social network research. At present, the research on social networks has been widely used in coauthor networks, citation networks, mobile social networks, enterprise knowledge management, and individual happiness, but there are few research studies on multilevel structure, dynamic research, complex network research, whole network research, and discussion network research. This provides space for future research on social networks.

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Moeinifar, V., i S. Gündüç. "Zealots' effect on opinion dynamics in complex networks". Mathematical Modeling and Computing 8, nr 2 (2021): 203–14. http://dx.doi.org/10.23939/mmc2021.02.203.

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In this paper, we study zealots' effects on social networks. Our social network is based on scale-free networks using Barabasi–Albert method and random networks using Erdős–Rényi method. We used a pre-studied modified Voter model that includes zealots, individuals who never change their opinions. We chose prominent individuals (i.e. hubs) as zealots. In this way we first chose important individuals with high degree (hubs); second, individuals with high closeness. And then examined the consensus time compared with that zealots are chosen as non-important individuals. We found that the time to get to the consensus state in social networks is the same for different numbers of zealots but with the same degrees of contamination with zealotry. For example, one zealot's effect with a degree of 64 is same to 8 zealots' effects with a degree of 8.

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Shao, Chenxi, i Yubing Duan. "Identifying community structure in complex networks". International Journal of Modern Physics B 29, nr 19 (21.07.2015): 1550131. http://dx.doi.org/10.1142/s0217979215501313.

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A wide variety of applications could be formulated to resolve the problem of finding all communities from a given network, ranging from social and biological network analysis to web mining and searching. In this study, we propose the concept of virtual attractive strength between each pair of node in networks, and then give the definition of community structure based on the proposed attractive strength. Furthermore, we present a community detection method by moving vertices to the clusters that produce the largest attractive strengths to them until the division of network reaches unchanged. Experimental results on synthetic and real networks indicate that the proposed approach has favorite effectiveness and fast convergence speed, which provides an efficient method for exploring and analyzing complex systems.

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., Sumitra, i Amaresh . "Impacts in Social Networks". International Journal for Research in Applied Science and Engineering Technology 10, nr 5 (31.05.2022): 982–86. http://dx.doi.org/10.22214/ijraset.2022.42307.

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Abstract: In this paper, we present a methodology both informal community construction and strength of impact between people advance continually, it expects to follow the powerful hubs under a unique setting. To resolve this issue, we investigate the Influential Node Tracking (INT) issue as an expansion to the conventional Influence Maximization issue (IM) under powerful interpersonal organizations. While Influence Maximization issue targets distinguishing a bunch of k hubs to boost the joint impact under one static organization, INT issue centers around following a bunch of persuasive hubs that continues to expand the impact as the organization advances. Using the perfection of the advancement of the organization structure, we propose a productive calculation, Upper Bound Interchange Greedy (UBI) and a variation, UBI+. Rather than developing the seed set from the beginning, begin from the compelling seed set we find beforehand and execute hub substitution to further develop the impact inclusion. Moreover, by utilizing a quick update technique by working out the minor addition of hubs, our calculation can scale to dynamic interpersonal organizations with a huge number of hubs. Exact examinations on three genuine huge scope dynamic informal communities show that our UBI and its variations, UBI+ accomplishes better execution with regards to both impact inclusion and running time. Keywords: Complex networks, complex systems, COVID-19, multiplex networks, optimization, social networks

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Weller, Susie. "Young people's social capital: complex identities, dynamic networks". Ethnic and Racial Studies 33, nr 5 (maj 2010): 872–88. http://dx.doi.org/10.1080/01419870903254653.

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Musial, Katarzyna, Piotr Bródka i Pasquale De Meo. "Analysis and Applications of Complex Social Networks 2018". Complexity 2019 (1.04.2019): 1–2. http://dx.doi.org/10.1155/2019/9082573.

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Nekovee, M., Y. Moreno, G. Bianconi i M. Marsili. "Theory of rumour spreading in complex social networks". Physica A: Statistical Mechanics and its Applications 374, nr 1 (styczeń 2007): 457–70. http://dx.doi.org/10.1016/j.physa.2006.07.017.

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Zhao, Q. J., i Z. M. Wen. "Integrative networks of the complex social-ecological systems". Procedia Environmental Sciences 13 (2012): 1383–94. http://dx.doi.org/10.1016/j.proenv.2012.01.131.

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Jalili, Mahdi. "Social power and opinion formation in complex networks". Physica A: Statistical Mechanics and its Applications 392, nr 4 (luty 2013): 959–66. http://dx.doi.org/10.1016/j.physa.2012.10.013.

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Hossain, Liaquat. "Social Networks on Dynamic and Complex Project Coordination". International Journal of Project Management 27, nr 5 (lipiec 2009): 433–34. http://dx.doi.org/10.1016/j.ijproman.2009.05.002.

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