  Community affiliation of a node plays an important role in determining its
contextual position in the network, which may raise privacy concerns when a
sensitive node wants to hide its identity in a network. Oftentimes, a target
community seeks to protect itself from adversaries so that its constituent
members remain hidden inside the network. The current study focuses on hiding
such sensitive communities so that the community affiliation of the targeted
nodes can be concealed. This leads to the problem of community deception which
investigates the avenues of minimally rewiring nodes in a network so that a
given target community maximally hides from a community detection algorithm. We
formalize the problem of community deception and introduce NEURAL, a novel
method that greedily optimizes a node-centric objective function to determine
the rewiring strategy. Theoretical settings pose a restriction on the number of
strategies that can be employed to optimize the objective function, which in
turn reduces the overhead of choosing the best strategy from multiple options.
We also show that our objective function is submodular and monotone. When
tested on both synthetic and 7 real-world networks, NEURAL is able to deceive 6
widely used community detection algorithms. We benchmark its performance with
respect to 4 state-of-the-art methods on 4 evaluation metrics. Additionally,
our qualitative analysis of 3 other attributed real-world networks reveals that
NEURAL, quite strikingly, captures important meta-information about edges that
otherwise could not be inferred by observing only their topological structures.
