Nowadays, many network representation learning algorithms and downstream network mining tasks have already paid attention to dynamic networks or temporal networks, which are more suitable for real-world complex scenarios by modeling evolving patterns and temporal dependencies between node interactions. Moreover, representing and mining temporal networks have a wide range of applications, such as fraud detection, social network analysis, and drug discovery. To contribute to the network representation learning and network mining research community, in this paper, we generate a new biological dataset of dynamic protein-protein interaction networks (i.e., DPPIN), which consists of twelve dynamic protein-level interaction networks of yeast cells at different scales. We first introduce the generation process of DPPIN. To demonstrate the value of our published dataset DPPIN, we then list the potential applications that would be benefited. Furthermore, we design dynamic local clustering, dynamic spectral clustering, dynamic subgraph matching, dynamic node classification, and dynamic graph classification experiments, where DPPIN indicates future research opportunities for some tasks by presenting challenges on state-of-the-art baseline algorithms. Finally, we identify future directions for improving this dataset utility and welcome inputs from the community. All resources of this work are deployed and publicly available at https://github.com/DongqiFu/DPPIN.

翻译：目前,许多网络代表性学习算法和下游网络采矿任务已经注意到动态网络或时间网络,这些网络或时间网络更适合现实世界复杂情景,通过对节点相互作用之间不断变化的模式和时间依赖性进行建模。此外,代表和采矿时间网络具有广泛的应用,例如欺诈检测、社会网络分析和药物发现。为了对网络代表性学习和网络采矿研究界作出贡献,我们在本文件中产生了一个新的生物数据集,由动态蛋白质-蛋白互动网络(即DPPPIN)组成,由不同规模的酵母12个动态蛋白级互动网络组成。我们首先引入DPPIN的生成过程。为了展示我们已公布的数据集DPPIN的价值,我们然后列出可能受益的潜在应用。此外,我们设计动态的本地集群、动态光谱组合、动态子图匹配、动态节点分类和动态图表分类实验,DPPININ通过对状态/艺术基准算法提出挑战,指出了某些任务的未来研究机会。最后,我们确定了DPIN的生成过程。我们确定了改进这一数据设置工具的未来方向,并欢迎社区提供这种工具。