Introduces ASI metric and null-model test to quantify angular separation of communities in geometric spaces and applies it to show temperature-induced dimensionality jumps and intrinsic dimension detection in hyperbolic networks.
A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities
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Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding
Introduces ASI metric and null-model test to quantify angular separation of communities in geometric spaces and applies it to show temperature-induced dimensionality jumps and intrinsic dimension detection in hyperbolic networks.