  In the big data era, the relationship between entries becomes more and more
complex. Many graph (or network) algorithms have already paid attention to
dynamic networks, which are more suitable than static ones for fitting the
complex real-world scenarios with evolving structures and features. To
contribute to the dynamic network representation learning and mining research,
we provide a new bunch of label-adequate, dynamics-meaningful, and
attribute-sufficient dynamic networks from the health domain. To be specific,
in our proposed repository DPPIN, we totally have 12 individual dynamic network
datasets at different scales, and each dataset is a dynamic protein-protein
interaction network describing protein-level interactions of yeast cells. We
hope these domain-specific node features, structure evolution patterns, and
node and graph labels could inspire the regularization techniques to increase
the performance of graph machine learning algorithms in a more complex setting.
Also, we link potential applications with our DPPIN by designing various
dynamic graph experiments, where DPPIN could indicate future research
opportunities for some tasks by presenting challenges on state-of-the-art
baseline algorithms. Finally, we identify future directions to improve the
utility of this repository and welcome constructive inputs from the community.
All resources (e.g., data and code) of this work are deployed and publicly
available at https://github.com/DongqiFu/DPPIN.
