dc.contributor.author |
Mittal, Shravika |
|
dc.contributor.author |
Chakraborty, Tanmoy (Advisor) |
|
dc.date.accessioned |
2021-05-25T08:33:26Z |
|
dc.date.available |
2021-05-25T08:33:26Z |
|
dc.date.issued |
2020-05-27 |
|
dc.identifier.uri |
http://repository.iiitd.edu.in/xmlui/handle/123456789/918 |
|
dc.description.abstract |
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 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 itself from a
community detection algorithm.
We formalize the problem 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 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. Our qualitative analysis on 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. |
en_US |
dc.language.iso |
en_US |
en_US |
dc.publisher |
IIIT-Delhi |
en_US |
dc.subject |
Community detection, community deception, safeness and permanence |
en_US |
dc.title |
Hide and seek: outwitting community detection algorithms |
en_US |
dc.type |
Other |
en_US |