On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
Identifying sets of key players in a social network
Computational & Mathematical Organization Theory
Robustness of centrality measures against link weight quantization in social network analysis
Proceedings of the Fourth Annual Workshop on Simplifying Complex Networks for Practitioners
Analysis of cluster formations on planer cells based on genetic programming
Computational & Mathematical Organization Theory
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This study investigates the topological form of a network and its impact on the uncertainty entrenched in descriptive measures computed from observed social network data, given ubiquitous data-error. We investigate what influence a network's topology, in conjunction with the type and amount of error, has on the ability of a measure, derived from observed data, to correctly approximate the same of the ground-truth network. By way of a controlled experiment, we reveal the differing effect that observation error has on measures of centrality and local clustering across several network topologies: uniform random, small-world, core-periphery, scale-free, and cellular. Beyond what is already known about the impact of data uncertainty, we found that the topology of a social network is, indeed, germane to the accuracy of these measures. In particular, our experiments show that the accuracy of identifying the prestigious, or key, actors in a network--according observed data--is considerably predisposed by the topology of the ground-truth network.