  Detecting anomalous subgraphs in a dynamic graph in an online or streaming
fashion is an important requirement in industrial settings for intrusion
detection or denial of service attacks. While only detecting anomalousness in
the system by edge frequencies is an optimal approach, many latent information
can get unnoticed in the process, since as a characteristic of the network only
edge frequencies are considered. We propose a game theoretic approach whereby
using the modularity function we try to estimate in a streaming graph
\emph{whether addition of a new edge in the current time tick results in a
dense subgraph creation, thus indicating possible anomalous score}. Our
contributions are as follows: (a) We propose a novel game-theoretic framework
for detecting dense subcommunities in an online streaming environment; (b) We
detect such subcommunities using constant memory storage. Our results are
corroborated with empirical evaluation on real datasets.
