Pálovics, Róbert and Benczúr, András (2017) Raising graphs from randomness to reveal information networks. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017. ACM Press, New York, pp. 23-32. ISBN 978-1-4503-4675-7 10.1145/3018661.3018664

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Abstract

We analyze the fine-grained connections between the aver- age degree and the power-law degree distribution exponent in growing information networks. Our starting observation is a power-law degree distribution with a decreasing expo- nent and increasing average degree as a function of the net- work size. Our experiments are based on three Twitter at- mention networks and three more from the Koblenz Network Collection. We observe that popular network models cannot explain decreasing power-law degree distribution exponent and increasing average degree at the same time. We propose a model that is the combination of exponential growth, and a power-law developing network, in which new "homophily" edges are continuously added to nodes propor- tional to their current homophily degree. Parameters of the average degree growth and the power-law degree distribu- tion exponent functions depend on the ratio of the network growth exponent parameters. Specifically, we connect the growth of the average degree to the decreasing exponent of the power-law degree distribution. Prior to our work, only one of the two cases were handled. Existing models and even their combinations can only reproduce some of our key new observations in growing information networks. © 2017 ACM.

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