Evaluating ensemble ranking fusion for multi-source localization in propagation graphs

Abstract

Source identification in propagation graphs is an inverse problem that aims to recover the initial infected or activated nodes from an observed propagation snapshot. This paper studies a static, structure-driven setting in which temporal traces, observer timestamps, and large labeled diffusion histories are unavailable. Voting and ranking-fusion methods are often motivated by the ensemble-learning intuition that several imperfect signals can be more useful after aggregation than any single signal alone. The study tests whether this intuition also helps multi-source localization on propagation graphs, where the individual signals are structural node heuristics rather than learned classifiers. The evaluation covers ten structural heuristics, four ensemble aggregation families, and four implemented source-localization baselines across 22 network-by-propagation cases and 198 saved propagation instances. The analysis separates fixed-method comparisons from diagnostic headroom, compares structural ensembles with non-structural source-localization baselines, and examines whether source counts can be estimated from score gaps or score thresholds rather than supplied as an oracle k. The results support a qualified conclusion. Structural ensembles expose a statistically reliable direction of improvement over selected single-heuristic references, but practical fixed-method gains are modest and depend on how member weights or configurations are chosen. Average-distance and topological centrality are the strongest recurring structural signals, suggesting that future methods should model proximity and graph-position evidence more explicitly rather than enumerate more voting combinations. Adaptive epsilon-gap and threshold rules partially control source-count error, but they do not remove the source-count problem under static snapshots. Overall, ensemble ranking fusion is useful as a calibrated diagnostic and sometimes as a practical aggregation layer, while richer temporal information, validation data, or more principled source-count estimators are still needed for robust deployment.