InfoMap is a popular approach to detect densely connected "communities" of nodes in networks. To detect such communities, InfoMap uses random walks and ideas from information theory. Motivated by the dynamics of disease spread on networks, whose nodes can have heterogeneous disease-removal rates, we adapt InfoMap to absorbing random walks. To do this, we use absorption-scaled graphs (in which edge weights are scaled according to absorption rates) and Markov time sweeping. One of our adaptations of InfoMap converges to the standard version of InfoMap in the limit in which the node-absorption rates approach $0$. We demonstrate that the community structure that one obtains using our adaptations of InfoMap can differ markedly from the community structure that one detects using methods that do not account for node-absorption rates. We also illustrate that the community structure that is induced by heterogeneous absorption rates can have important implications for susceptible-infected-recovered (SIR) dynamics on ring-lattice networks. For example, in some situations, the outbreak duration is maximized when a moderate number of nodes have large node-absorption rates.

翻译：InfoMap是一种流行的网络节点密集连接"社区"检测方法。该方法通过随机游走和信息论思想来识别此类社区。受网络疾病传播动力学（其中节点具有异质性移除率）的启发，我们将InfoMap方法适配至吸收随机游走。为此，我们采用吸收缩放图（根据吸收率缩放边权）与马尔可夫时间扫描技术。我们的改进型InfoMap在节点吸收率趋近于$0$的极限条件下，能够收敛至标准版InfoMap。实验证明，采用本适配方法获得的社区结构与忽略节点吸收率的传统检测方法所得结果存在显著差异。我们还揭示，异质性吸收率诱发的社区结构对环状晶格网络上的易感-感染-恢复（SIR）动力学具有重要影响。例如，在某些情境下，当适中数量的节点具有较大吸收率时，疫情暴发持续时间达到最大值。