Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains users with transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.

中文翻译：

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大型复杂属性网络上的增量社区检测

网络数据的社区检测是一项基础性任务，在工业上有很多应用。工业中的网络数据可能非常庞大，属性不完整且复杂，更重要的是还在不断增长。这需要一种社区检测技术，该技术能够处理大规模网络上的属性和拓扑信息，并且是增量的。在本文中，我们提出了 inc-AGGMMR，这是一个增量社区检测框架，能够有效地解决来自可扩展性、混合属性、不完整值和网络演进的挑战。通过构建增强图，我们通过引入属性中心和归属边将属性映射到网络中。然后通过模块化最大化来检测社区。在这个过程中，我们调整归属边的权重以平衡属性和拓扑信息对社区检测的贡献。权重调整机制实现了所有顶点的社区成员的增量更新。我们根据八个强基线在五个基准数据集上评估 inc-AGGMMR。我们还提供了一个案例研究，以增量检测 PayPal 支付网络上的社区，其中包含 用户 交易。结果证明了inc-AGGMMR的有效性和实用性。