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In many simulation studies involving networks there is the need to rely on asample network to perform the simulation experiments. In many cases, realnetwork data is not available due to privacy concerns. In that case we canrecourse to synthetic data sets with similar properties to the real data. Inthis paper we discuss the problem of generating synthetic data sets for acertain kind of online social network, for simulation purposes. Some popularonline social networks, such as LinkedIn and ResearchGate, allow userendorsements for specific skills. For each particular skill, the endorsementsgive rise to a directed subgraph of the corresponding network, where the nodescorrespond to network members or users, and the arcs represent endorsementrelations. Modelling these endorsement digraphs can be done by formulating anoptimization problem, which is amenable to different heuristics. Ourconstruction method consists of two stages: The first one simulates the growthof the network, and the second one solves the aforementioned optimizationproblem to construct the endorsements.

Synthetic Generation of Social Network Data With Endorsements