Network Science and Graph Theory

research; network science; data mining

Exploring the structure and function of temporal networks with dynamic graphlets

Motivation: 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.


Improving Identification of Key Players in Aging via Network De-Noising and Core Inference

Motivation: Since human aging is hard to study experimentally due to long lifespan and ethical constraints, current “ground truth” knowledge about human aging has been obtained by transferring aging-related knowledge from well-studied model species via sequence homology or by studying human gene expression data. Since proteins function by interacting with each other, analyzing protein-protein interaction (PPI) networks in the context of aging is promising. Unlike existing static network research of aging, since cellular functioning is dynamic, we recently integrated the static human PPI network with aging-related gene expression data to form dynamic, age-specific networks. Then, we predicted as key players in aging those proteins whose network topologies significantly changed with age. Since current networks are noisy, here, we use link prediction to de-noise the human network and predict improved key players in aging from the de-noised data. Indeed, de-noising gives more significant overlap between the predicted data and the “ground truth” aging-related data. Yet, we obtain novel predictions, which we validate in the literature. Also, we improve the predictions by an alternative strategy: removing “redundant” edges from the age-specific networks and using the resulting age-specific network “cores” to study aging. We produce new knowledge from dynamic networks encompassing multiple data types, via network de-noising or core inference, complementing the existing knowledge obtained from sequence or expression data.