论文标题
DYCSC：基于群集结构的动态网络的进化过程建模
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster Structure
论文作者
论文摘要
时间网络是一种重要类型的网络类型，其拓扑结构会随着时间而变化。与静态网络上的方法相比，时间网络嵌入（TNE）方法面临三个挑战：1）它无法描述整个网络快照的时间依赖性； 2）潜在空间中的节点嵌入未表明网络拓扑的变化； 3）它无法通过一系列快照上的参数继承来避免大量的冗余计算。为此，我们提出了一种名为“动态群集结构约束模型”（DYCSC）的新型时间网络嵌入方法，其核心思想是通过对网络中的节点趋势对给定数量的群集施加时间限制来捕获时间网络的演变。它不仅生成了节点的低维嵌入向量，还可以保留时间网络的动态非线性特征。多个REALWORLD数据集的实验结果证明了DYCSC对于时间图嵌入的优越性，因为在多个时间链路预测任务中，它始终优于竞争方法，从而超过了竞争方法。此外，消融研究进一步验证了所提出的时间约束的有效性。
Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal dependence across network snapshots; 2) the node embedding in the latent space fails to indicate changes in the network topology; and 3) it cannot avoid a lot of redundant computation via parameter inheritance on a series of snapshots. To this end, we propose a novel temporal network embedding method named Dynamic Cluster Structure Constraint model (DyCSC), whose core idea is to capture the evolution of temporal networks by imposing a temporal constraint on the tendency of the nodes in the network to a given number of clusters. It not only generates low-dimensional embedding vectors for nodes but also preserves the dynamic nonlinear features of temporal networks. Experimental results on multiple realworld datasets have demonstrated the superiority of DyCSC for temporal graph embedding, as it consistently outperforms competing methods by significant margins in multiple temporal link prediction tasks. Moreover, the ablation study further validates the effectiveness of the proposed temporal constraint.
