Efficient Personalized Community Detection via Genetic Evolution

TL;DR

This paper introduces a novel genetic algorithm-based method for personalized community detection in graphs, achieving higher resolution and efficiency compared to existing approaches, suitable for real-time applications.

Contribution

It presents a new hybrid offline-online genetic model with a distributed implementation for fast, personalized community detection tailored to user needs.

Findings

Outperforms state-of-the-art methods in accuracy and speed.

Significantly reduces running time in large-scale datasets.

Effective in real-time personalized community detection scenarios.

Abstract

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.