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  Subjects -> STATISTICS (Total: 130 journals)
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Advances in Complex Systems
Journal Prestige (SJR): 0.25
Citation Impact (citeScore): 1
Number of Followers: 10  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0219-5259 - ISSN (Online) 1793-6802
Published by World Scientific Homepage  [121 journals]
  • INFLUENCE OF NETWORK STRUCTURE AND AGENT PROPERTY ON SYSTEM PERFORMANCE

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      Authors: HONGZHONG DENG, JI LI, HONGQIAN WU, BINGFENG GE
      Abstract: Advances in Complex Systems, Ahead of Print.
      System structure can affect or decide the system function. Many pioneers have analyzed the impact of system’s macro-statistical characteristics, such as degree distribution and giant component, on system performance. But only few research works were conducted on the relation of mesoscopic structure and agent property with system task performance. In this paper, we designed a scenario that, in a multiagent system, agents will try their best to form a qualified team to fulfill more system tasks under the requirements from agent property, structure and task. The theoretical and simulation results show that the agent link network, agent properties and task requirement will co-affect the dynamic team formation and at last have serious effects on a system’s task completion ratio and performance. Some factors such as network density and task introduction period have positive influence. Task execution time and team size have negative influence. Some factors show a counter-intuitive influence. The clustering coefficient has not much influence as people expected and the task publicity time isn’t bigger the better. Notably, system performance is affected by the coupling effect, instead of the independent effects of all factors. The effect of system structure on system function conditionally relies on the support from agent ability and task requirement.
      Citation: Advances in Complex Systems
      PubDate: 2024-01-23T08:00:00Z
      DOI: 10.1142/S021952592350011X
       
  • ROUTING STRATEGIES FOR SUPPRESSING TRAFFIC-DRIVEN EPIDEMIC SPREADING IN
           MULTIPLEX NETWORKS

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      Authors: JINLONG MA, TINGTING XIANG, MINGWEI CAI
      Abstract: Advances in Complex Systems, Ahead of Print.
      Multiplex networks have proven to be valuable tools for modeling and analyzing real complex system. Extensive work has been done on the traffic dynamics on multiplex networks, but there remains a lack of sufficient attention towards studying routing strategies for the purpose of suppressing epidemic spreading. In this paper, the impact of global awareness routing (GAR), improved global awareness routing (IGAR), and improved active routing (IAR) strategies on traffic-driven epidemic spreading are investigated. Our findings indicate that in the case of infinite node-delivery capacity and no traffic congestion in the network, adjusting routing parameters can effectively suppress epidemic spreading. In this context, these three strategies show better abilities on the multiplex network built by WS or ER model to minimize the density of infected nodes, thus contributing to the overall inhibition of the epidemic spread. However, in the multiplex network constructed by BA model, GAR strategy has a promoting effect on epidemic spreading compared with the shortest routing strategy. In addition, by controlling traffic flow, limiting node delivery capabilities can contain outbreaks. Our results suggest that adopting appropriate routing strategies in multiplex networks can play a proactive role in controlling epidemic spreading. This is crucial for formulating effective prevention and control measures and improving public health security.
      Citation: Advances in Complex Systems
      PubDate: 2024-01-18T08:00:00Z
      DOI: 10.1142/S0219525923400052
       
  • IDENTIFYING VITAL NODES IN COMPLEX NETWORK BY CONSIDERING MULTIPLEX
           INFLUENCES

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      Authors: TAO REN, YANJIE XU, LINGJUN LIU, ENMING GUO, PENGYU WANG
      Abstract: Advances in Complex Systems, Ahead of Print.
      Identifying vital nodes is a fundamental topic in network science. Some methods are proposed to identify vital nodes in a complex network. These measures take into account different aspects of a node’s importance, such as its number of connections, the centrality of its connected nodes, and the distribution of its connections. Applying these measures makes it is possible to identify the nodes that play a vital role in the network and that have the greatest impact on its structure and function. However, there is still an inherent problem with identifying vital nodes accurately and discriminatively. To address the problem, for undirected unweighted networks, we propose an algorithm based on the nodes’ multiplex influences via the network structure to identify vital nodes. The effectiveness of the proposed method is evaluated by Kendall’s Tau ([math]) and monotonicity and compared with well-known existing metrics such as degree centrality, K-shell decomposition, H-index, betweenness centrality, closeness centrality, eigenvector centrality, collective influence, and gravity model in 10 real networks. Experimental results show the superiority of the proposed algorithm in identifying vital nodes.
      Citation: Advances in Complex Systems
      PubDate: 2023-12-08T08:00:00Z
      DOI: 10.1142/S0219525923500091
       
  • AN EVOLUTIONARY MODEL OF SOCIAL NETWORK STRUCTURE DRIVEN BY INFORMATION
           INTERACTION

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      Authors: FUZHONG NIAN, JIANJIAN ZHOU, YINUO QIAN
      Abstract: Advances in Complex Systems, Ahead of Print.
      The interaction of information and the evolution of network structure are inseparable. In order to construct social network evolution and information propagation models that better fit real-world scenarios, this paper proposes a social network structure evolution model driven by changes in the strength of relationships between individuals through their information interactions with each other. During the evolution process of the network, information interaction between individuals is also influenced by the network structure. Therefore, we improve traditional propagation models and construct an information propagation model with dynamic propagation rates. The proposed model is used to simulate both the spread of information and the evolution of network structures in real social networks. Finally, simulation results are compared to real-world data, demonstrating the effectiveness and rationality of the proposed model.
      Citation: Advances in Complex Systems
      PubDate: 2023-11-28T08:00:00Z
      DOI: 10.1142/S0219525923500108
       
  • STRUCTURAL PROPERTIES OF CORE–PERIPHERY COMMUNITIES

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      Authors: JUNWEI SU, PETER MARBACH
      Abstract: Advances in Complex Systems, Ahead of Print.
      Empirical studies have consistently demonstrated the presence of a core–periphery structure within social network communities. Nevertheless, a formal model and comprehensive analysis to fully understand the structural characteristics of these communities are still lacking. This paper seeks to characterize these properties, focusing on agents’ interconnections and their allocation of rates. Employing a game-theoretic approach, our analysis unveils several novel insights. First, we show that periphery agents not only follow core agents but also other periphery agents who share similar primary interests. Second, our results illuminate the emergence of core–periphery communities, revealing the conditions under which they form, and how they form.
      Citation: Advances in Complex Systems
      PubDate: 2023-11-22T08:00:00Z
      DOI: 10.1142/S0219525923400040
       
  • LUCK OF OUTCOME IN THE TALENT VERSUS LUCK MODEL

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      Authors: HIROSHI HAMADA
      Abstract: Advances in Complex Systems, Ahead of Print.
      This paper analyzes the Talent versus Luck model, which examines the impact of talent and luck on an individual’s career success. The original simulation-based model demonstrated that the distribution of capital has a heavy tail, and the most successful individuals are not necessarily the most talented. While the implications of the original model are intriguing, those findings were based solely on numerical calculations, and it was unclear how generally valid they are. Challet et al. generalize the original model using an analytical approach and successfully clarify the relationship between talent, lucky events, and capital when talent is constant and follows a uniform distribution. We reformulate a simplified model and derive more general propositions about the relationship between luck and talent in individual success by introducing the new concept of luck of outcome in addition to the luck of opportunity in previous models. We show that the capital distribution generated from a simplified talent versus luck model follows a lognormal distribution even when the talent is subject to a normal distribution. Moreover, we specify the relationship between the inequality of the distribution, which is indicated by the Gini coefficient, and the parameters of talent distribution.
      Citation: Advances in Complex Systems
      PubDate: 2023-11-18T08:00:00Z
      DOI: 10.1142/S021952592350008X
       
  • HYPERMATRIX ALGEBRA AND IRREDUCIBLE ARITY IN HIGHER-ORDER SYSTEMS:
           CONCEPTS AND PERSPECTIVES

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      Authors: CARLOS ZAPATA-CARRATALÁ, MAXIMILIAN SCHICH, TALIESIN BEYNON, XERXES D. ARSIWALLA
      Abstract: Advances in Complex Systems, Ahead of Print.
      Theoretical and computational frameworks of complexity science are dominated by binary structures. This binary bias, seen in the ubiquity of pair-wise networks and formal binary operations in mathematical models, limits our capacity to faithfully capture irreducible polyadic interactions in higher-order systems. A paradigmatic example of a higher-order interaction is the Borromean link of three interlocking rings. In this paper, we propose a mathematical framework via hypergraphs and hypermatrix algebras that allows to formalize such forms of higher-order bonding and connectivity in a parsimonious way. Our framework builds on and extends current techniques in higher-order networks — still mostly rooted in binary structures such as adjacency matrices — and incorporates recent developments in higher-arity structures to articulate the compositional behavior of adjacency hypermatrices. Irreducible higher-order interactions turn out to be a widespread occurrence across natural sciences and socio-cultural knowledge representation. We demonstrate this by reviewing recent results in computer science, physics, chemistry, biology, ecology, social science, and cultural analysis through the conceptual lens of irreducible higher-order interactions. We further speculate that the general phenomenon of emergence in complex systems may be characterized by spatio-temporal discrepancies of interaction arity.
      Citation: Advances in Complex Systems
      PubDate: 2023-11-09T08:00:00Z
      DOI: 10.1142/S0219525923500078
       
 
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