With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships. We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research.

Reference: Yuriy Hulovatyy, Huili Chen, and Tijana Milenkovic. "Exploring the structure and function of temporal networks with dynamic graphlets." Bioinformatics, 31(12): i171-i180, 2015. Also, in Proceedings of the 23rd International Conference on Intelligent Systems for Molecular Biology and 14th European Conference on Computational Biology (ISMB/ECCB), Dublin, Ireland, July 10-14, 2015, accepted (acceptance rate: ~18%).

Software: The executable of our dynamic graphlet counting code is available here, along with detailed usage instructions. Given a temporal network as input, this executable produces dynamic graphlet counts both for the entire network and individual nodes.

Data: Data used for node classification is available here and data used for aging analysis is available here. These are the original data sources.

Results: The list of our predicted aging-related genes is available here.