الفهرس 
Some Basics of Graph TheoryOnly 14 pages are availabe for public view
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Abstract
Centrality measure is one of the most important issues in social network analysis, which is the process of extracting the most important central member of the network. The main goal of this measure is assessing the importance of a node’s position in the network. In this work, we investigate a set of centrality measures for weighted dynamic networks. First, the Temporal Communication Centrality is defined to reflect the communication ability of a node in a weighted dynamic network. Also, a derivative measure related to the temporal communication centrality measure is proposed and called iterative temporal communication centrality. Second, Temporal Degree Centrality is generalized to weighted networks to find the nodes which have many connection with high weights. Also, Temporal Degree-Degree Centrality is proposed to explore the nodes which are connected to the nodes with high degree centrality values over time. Temporal Closeness Centrality is extended to the case of weighted networks to explore the nodes which can spread information in the weighted dynamic networks. Finally, a new centrality measure is proposed to determine which nodes are close to many nodes of high temporal closeness centrality. This measure is called Temporal Closeness-Closeness Centrality. All these measures are applied on a modified dynamic model called Time-Ordered Weighted Graph. This model reduces the complexity of a dynamic weighted network by using directed flows. Also, we use a dataset to apply all the proposed measures; the results ensure the possibility of using these measures for dynamic weighted networks.