Identification of Starting Points in Sampling of Complex Networks

Physics – Computational Physics

Scientific paper

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17 pages, 11 figures and 1 table

Scientific paper

Because of their large size, several real-world complex networks can only be represented and computationally handled in a sampled version. One of the most common sampling schemes is breadth-first where, after starting from a given node, its neighbors are taken, and then the neighbors of neighbors, and so on. Therefore, it becomes an important issue, given a specific network sampled by the breadth-first method, to try to identify the original node. In addition to providing a clue about how the sampling was performed, the identification of the original node also paves the way for the reconstruction of the sampling dynamics provided the original networks is available. In the current article, we propose and validate a new and effective methodology for the identification of the original nodes. The method is based on the calculation of the accessibility of the nodes in the sampled network. We show that the original node tends to have the highest values of accessibility. The potential of the methodology is illustrated with respect to three theoretical complex networks model as well as a real-world network, namely the United States patent network.

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