Assessing Linked Data Mappings using Network Measures Christophe Guéret, Paul Groth, Claus Stadler, and Jens Lehmann Linked Data is at its core about the setting of links between resources. Links provide enriched semantics, pointers to extra information and enable the merging of data sets. However, as the amount of Linked Data has grown, there has been the need to automate the creation of links and such automated approaches can create low-quality links or unsuitable network structures. In particular, it is difficult to know whether the links introduced improve or diminish the quality of Linked Data. In this paper, we present an extensible framework and a series of justified network measures that allow for the assessment of Linked Data mappings. We test the framework on a set of known good and bad links generated by common mapping systems and discuss which network measures show promise for use in assessment.