Statistical Inference on Random Graphs: Comparative Power Analyses via Monte Carlo
نویسندگان
چکیده
We present a comparative power analysis, via Monte Carlo, of various graph invariants used as statistics for testing graph homogeneity versus a “chatter” alternative – the existence of a local region of excessive activity. Our results indicate that statistical inference on random graphs, even in a relatively simple setting, can be decidedly non-trivial. We find that none of the graph invariants considered is uniformly most powerful throughout our space of alternatives.
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