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Wiring the Past: A Network Science Perspective on the Challenge of Archeological Similarity Networks

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Nowadays, it is a common knowledge that scholars from different disciplines, regardless of the specificities of their research domains, can find in network science a valuable ally when tackling complexity. However, there are many difficulties that may arise, starting from the process of mapping a system onto a network which is not by any means a trivial step. This article deals with those issues inherent to the specific challenge of building a network from archeological data, focusing in particular on networks of archeological contexts. More specifically, we address technical difficulties faced when constructing networks of contexts or sites where past interactions are inferred based on some kind of similarity between the corresponding assemblages (Archeological Similarity Networks or ASN). We propose a basic characterization in formal terms of ASN as a well-defined class of networks with its own specific features. Throughout the article, we devote special attention to the problem of quantifying the similarity between sites, especially in relation with the ubiquitous issues of data incompleteness and the reliability of the inferred ties. We argue that, generally speaking, human past studies are quite disconnected from the rest of interdisciplinary applications of network science and that this prevent this field from fully exploiting the potential of such methods. Our goal is to give hints about which are the interesting questions that archeological applications put on the table of network scientists. We suggest that such questions need to be translated into formal terms in order to be properly addressed within the framework of interdisciplinary collaborations. At this aim, a computational experiment is devised as an illustrative example of how simple models can help the cause.

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Frontiers in Digital Humanities, 2017, vol. 4, num. 13

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Articles publicats en revistes (Física de la Matèria Condensada)

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Wiring the Past: A Network Science Perspective on the Challenge of Archeological Similarity Networks