Abstract
Complex networks are a popular and frequent tool for modeling a variety of entities and their relationships. Understanding these relationships and selecting which data will be used in their analysis is key to a proper characterization. Most of the current approaches consider all available information for analysis, aggregating it over time. In this work, we studied the impact of such aggregation while characterizing complex networks. We model four real complex networks using an extended graph model that enables us to quantify the impact of the information aggregation over time. We conclude that data aggregation may distort the characteristics of the underlying real-world network and must be performed carefully.
Partially supported by CNPq, Finep, Fapemig, and CNPq/CT-Info/InfoWeb.
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Mourão, F., Rocha, L., Miranda, L., Almeida, V., Meira, W. (2009). Quantifying the Impact of Information Aggregation on Complex Networks: A Temporal Perspective. In: Avrachenkov, K., Donato, D., Litvak, N. (eds) Algorithms and Models for the Web-Graph. WAW 2009. Lecture Notes in Computer Science, vol 5427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-95995-3_5
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DOI: https://doi.org/10.1007/978-3-540-95995-3_5
Publisher Name: Springer, Berlin, Heidelberg
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