Abstract
Recent work has demonstrated that many networks have broad distributions of vertex degree. Here we show that this has a substantial impact on the shape of ego-centered networks and on concepts and methods based on ego-centered networks, such as snowball sampling and the “ripple effect”. In particular, we argue that one’s acquaintances, one’s immediate neighbors in the acquaintance network, are far from being a random sample of the population, and that this biases the numbers of neighbors two and more steps away. We demonstrate this concept using data on academic collaboration networks.
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