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    Centrality of Nodes with Karci Entropy
    (Ieee, 2018) Tugal, Ihsan; Karci, Ali
    A measure of centrality can be used to identify important assets that affect a system. In this study, the most used centrality measures degree, closeness, betweenness, eigenvector centrality and entropy centrality were used to identify the most effective nodes. The central/influential nodes can be detected more accurately by the Karci entropy which has just begun to be used new in social networks. Karci entropy contain Shannon when a equal 1. The more accurate results were obtained when the a coefficient in Karci entropy was correctly selected. The effect of node degree and edge weights to the network were measured together. The applicability of the entropy-based method for the detection of the most effective nodes in weighted networks has been demonstrated. The success of proposed method has been offered by comparison with traditional methods.
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    Comparisons of Karci and Shannon entropies and their effects on centrality of social networks
    (Elsevier, 2019) Tugal, Ihsan; Karci, Ali
    In order to measure the amount of different information in a system, entropy concept can be used. Graph entropy measures nodes' contribution to the entropy of the graph. By this way, the influential actors can be identified. Due to this case, a new entropy-based method was proposed to identify the influential actors. Karci entropy was applied to the social networks first time. The alpha parameter allowed us to combine many different conditions together when measuring in the network. The other important contribution of this paper is to predict the value of alpha parameter of Karci entropy by using fuzzy logic. After that Karci and Shannon entropies were compared based on experimental results. Moreover, Kara entropy was compared to traditional centrality measures. If Karci entropy definition is considered as a set of entropies, Shannon entropy can be regarded as an element of this set. Accordingly, it can be concluded that Karci entropy is superior to Shannon entropy. (C) 2019 Elsevier B.V. All rights reserved.