论文标题
通过遗传进化有效的个性化社区检测
Efficient Personalized Community Detection via Genetic Evolution
论文作者
论文摘要
个性化的社区检测旨在产生与用户需求相关的社区，这使许多下游任务（例如节点建议和用户的链接预测等）受益。它非常重要，但在以前的研究中缺乏足够的关注，这些研究涉及用户不依赖于用户，与用户不依赖于用户的，半手光，半企业或顶级的用户居住社区的社区检测。同时，由于复杂的图形结构，他们的大多数模型都是耗时的。与这些主题不同，个性化的社区检测需要提供与用户需求更相关的节点的高分辨率分区，而在剩余的较少相关节点上进行了更粗糙的方式分区。在本文中，为了以有效的方式解决此任务，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，将独立的社区结构编码为二进制树。随后，采用了在线遗传修剪步骤将树划分为社区。为了加速速度，我们还部署了模型的分布式版本，以在并行环境下运行。多个数据集上的大量实验表明，我们的模型的运行时间大大优于最新工厂。
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.
