基于遗传进化的高效个性化社区发现
Efficient Personalized Community Detection via Genetic Evolution
摘要：
个性化社区发现的目的是在图上生成与用户需求相关联的社区，从而为用户进行节点推荐和链接预测等下游任务提供便利。在以往的研究中，个性化社区发现具有重要的意义，但缺乏足够的关注，主要集中在与用户无关的、半监督的、以top-K用户为中心的社区发现等方面。同时，由于图结构复杂，大多数模型都是耗时的。与这些主题不同的是，个性化社区发现需要在与用户需求相关性更高的节点上提供更高分辨率的划分，而在其余相关性较低的节点上提供更粗糙的划分。在本文中，为了有效地解决这个问题，我们提出了一个遗传模型，包括离线和在线步骤。在离线步骤中，用户独立的社区结构被编码为二叉树。随后，应用在线遗传修剪步骤将树划分为社区。为了加快速度，我们还部署了一个分布式版本的模型在并行环境下运行。在多个数据集上进行的大量实验表明，我们的模型在运行时间显著减少的情况下优于最先进的模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
