  Hidden community is a useful concept proposed recently for social network
analysis. To handle the rapid growth of network scale, in this work, we explore
the detection of hidden communities from the local perspective, and propose a
new method that detects and boosts each layer iteratively on a subgraph sampled
from the original network. We first expand the seed set from a single seed node
based on our modified local spectral method and detect an initial dominant
local community. Then we temporarily remove the members of this community as
well as their connections to other nodes, and detect all the neighborhood
communities in the remaining subgraph, including some "broken communities" that
only contain a fraction of members in the original network. The local community
and neighborhood communities form a dominant layer, and by reducing the edge
weights inside these communities, we weaken this layer's structure to reveal
the hidden layers. Eventually, we repeat the whole process and all communities
containing the seed node can be detected and boosted iteratively. We
theoretically show that our method can avoid some situations that a broken
community and the local community are regarded as one community in the
subgraph, leading to the inaccuracy on detection which can be caused by global
hidden community detection methods. Extensive experiments show that our method
could significantly outperform the state-of-the-art baselines designed for
either global hidden community detection or multiple local community detection.
