Sequential stacking link prediction algorithms for temporal networks.

ABSTRACT: Link prediction algorithms are indispensable tools in many scientific applications by speeding up network data collection and imputing missing connections. However, in many systems, links change over time and it remains unclear how to optimally exploit such temporal information for link predictions in such networks. Here, we show that many temporal topological features, in addition to having high computational cost, are less accurate in temporal link prediction than sequentially stacked static network features. This sequential stacking link prediction method uses 41 static network features that avoid detailed feature engineering choices and is capable of learning a highly accurate predictive distribution of future connections from historical data. We demonstrate that this algorithm works w

SUBMITTER: He X 

PROVIDER: S-EPMC10866871 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature