  Personalized community detection aims to generate communities associated with
user need on graphs, which benefits many downstream tasks such as node
recommendation and link prediction for users, etc. It is of great importance
but lack of enough attention in previous studies which are on topics of
user-independent, semi-supervised, or top-K user-centric community detection.
Meanwhile, most of their models are time consuming due to the complex graph
structure. Different from these topics, personalized community detection
requires to provide higher-resolution partition on nodes that are more relevant
to user need while coarser manner partition on the remaining less relevant
nodes. In this paper, to solve this task in an efficient way, we propose a
genetic model including an offline and an online step. In the offline step, the
user-independent community structure is encoded as a binary tree. And
subsequently an online genetic pruning step is applied to partition the tree
into communities. To accelerate the speed, we also deploy a distributed version
of our model to run under parallel environment. Extensive experiments on
multiple datasets show that our model outperforms the state-of-arts with
significantly reduced running time.
