Using Causality-Aware Graph Neural Networks to Predict Temporal
  Centralities in Dynamic Graphs
Franziska Heeg Ingo Scholtes
Node centralities play a pivotal role in network science, social network
analysis, and recommender systems. In temporal data, static path-based
centralities like closeness or betweenness can give misleading results about
the true importance of nodes in a temporal graph. To address this issue,
temporal generalizations of betweenness and closeness have been defined that
are based on the shortest time-respecting paths between pairs of nodes.
However, a major issue of those generalizations is that the calculation of such
paths is computationally expensive. Addressing this issue, we study the
application of De Bruijn Graph Neural Networks (DBGNN), a causality-aware graph
neural network architecture, to predict temporal path-based centralities in
time series data. We experimentally evaluate our approach in 13 temporal graphs
from biological and social systems and show that it considerably improves the
prediction of both betweenness and closeness centrality compared to a static
Graph Convolutional Neural Network.