Abstract
Social networks are a source of large scale graphs. We study how social network algorithms behave on sparsified versions of such networks with two motivations in mind: 1. In practice, it is challenging to collect, store and process the entire often constantly growing network, so it is important to understand how algorithms behave on incomplete views of a network. 2. Even if one has the full network, algorithms may be infeasible at such large scale, and the only option may be to sparsify the networks to make them computationally tractable while still maintaining the fidelity of the social network algorithms.
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