List of subject articles Complex Networks


    • Open Access Article

      1 - A Multi-objective Multi-agent Optimization Algorithm for the Community Detection Problem
      Amirhossein Hosseinian Vahid Baradaran
      This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, whil Full Text
      This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, while they have a lower density of between-group connections. Due to high practical usage of community detection in scientific fields, many researchers developed different algorithms to meet various scientific requirements. However, single-objective optimization algorithms may fail to detect high quality communities of complex networks. In this paper, a novel multi-objective Multi-agent Optimization Algorithm, named the MAOA is proposed to detect communities of complex networks. The MAOA aims to optimize modularity and community score as objective functions, simultaneously. In the proposed algorithm, each feasible solution is considered as an agent and the MAOA organizes agents in multiple groups. The MAOA uses new search operators based on social, autonomous and self-learning behaviors of agents. Moreover, the MAOA uses the weighted sum method (WSM) in finding the global best agent and leader agent of each group. The Pareto solutions obtained by the MAOA is evaluated in terms of several performance measures. The results of the proposed method are compared with the outputs of three meta-heuristics. Experiments results based on five real-world networks show that the MAOA is more efficient in finding better communities than other methods. Manuscript Document
    • Open Access Article

      2 - Providing a Network for Measuring the Dynamics Volatility Connectedness of Oil and Financial Markets
      Nasser Gholami Teymor Mohammadi Hamid Amadeh Morteza  Khorsandi
      Various studies have shown that markets are not separated and that fluctuations in different markets affect each other. Therefore, awareness of connectedness is needed for investors and policymakers for making appropriate decisions. The aim of this paper is to measure t Full Text
      Various studies have shown that markets are not separated and that fluctuations in different markets affect each other. Therefore, awareness of connectedness is needed for investors and policymakers for making appropriate decisions. The aim of this paper is to measure the dynamics connectedness of selected stock markets in the Middle East, oil markets, gold, the dollar index, and euro-dollar and pound-dollar exchange rates during the period February 2007 to August 2019 in networks with different weekly horizons. In this paper, we intend to evaluate the pairwise impact of crude oil and the Middle East stock markets, in particular on the Tehran Stock Exchange, and to analyze this variance using different time horizons. The results show that in all time horizons the variance of forecast error in most markets is due to the shocks themselves. The Saudi Arabian Stock Exchange has the most impact on other Middle Eastern stocks. The dynamics connectedness of the oil markets is remarkable, however, as the time horizon increases, dynamic connectedness between the two markets decreases and they are mostly affected by other markets, especially the Middle East stock exchanges except for Iran. Moreover, Iran stock market is an isolated market. About the gold market, there is a significant connectedness with the pound-dollar exchange rate and gold market; however, the dynamics connectedness of this market with other markets are not significant. Therefore, this market and Iran stock exchange can be used as a tool to hedge risk for investors. Manuscript Document
    • Open Access Article

      3 - Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection
      Mohammad Pouya Salvati Jamshid  Bagherzadeh Mohasefi Sadegh Sulaimany
      Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A maj Full Text
      Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms. Manuscript Document
    • Open Access Article

      4 - Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks
      Alireza Mansouri Fattaneh Taghiyareh
      People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marke Full Text
      People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marketing. The interactions could be face-to-face or via online social networks. The social opinion phases are categorized into consensus, majority, and non-majority. In this research, we study phase transitions due to interactions between connected people with various noise levels using agent-based modeling and a computational social science approach. Two essential factors affect opinion formations: the opinion formation model and the network topology. We assumed the social impact model of opinion formation, a discrete binary opinion model, appropriate for both face-to-face and online interactions for opinion formation. For the network topology, scale-free networks have been widely used in many studies to model real social networks, while recent studies have revealed that most social networks fit log-normal distributions, which we considered in this study. Therefore, the main contribution of this study is to consider the log-normal distribution network topology in phase transitions in the social impact model of opinion formation. The results reveal that two parameters affect the phase transition: noise level and segregation. A non-majority phase happens in equilibrium in high enough noise level, regardless of the network topology, and a majority phase happens in equilibrium in lower noise levels. However, the segregation, which depends on the network topology, affects opinion groups’ population. A comparison with the scale-free network topology shows that in the scale-free network, which have a more segregated topology, resistance of segregated opinion groups against opinion change causes a slightly different phase transition at low noise levels. EI (External-Internal) index has been used to measure segregations, which is based on the difference between between-group (External) links and within-group (Internal) links. Manuscript Document