DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data

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 repository of dynamic protein-protein interaction network data (i.e., DPPIN), which consists of twelve dynamic network datasets describing protein-level interactions of yeast cells at different scales. We first introduce the generation process of DPPIN. To demonstrate the value of our published repository 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 network datasets of DPPIN could indicate future research opportunities for some tasks by presenting challenges on state-of-the-art baseline algorithms. Finally, we identify future directions for improving the utility of this repository and welcome constructive inputs from the community. All resources of this work are deployed and publicly available at https://github.com/DongqiFu/DPPIN.