Towards Automatic Evolution Tree Generation from Citation Graphs

Abstract:Surveys remain the primary way researchers grasp the lineage of methods within an AI subfield, but they scale poorly against the current rate of publication. Existing taxonomy-induction methods are largely leaf-bound and time-agnostic; they tend to force transitional papers into mature leaves and can create topological inversions between ancestors and descendants. We propose EvoTree, a staged framework that decouples conceptual backbone learning from temporal refinement: a graph-aware encoder with distribution-based hierarchical clustering yields a stable taxonomy backbone; temporal fine-tuning then re-attaches marginal papers to internal nodes under monotonic-path constraints; a final LLM pass labels concepts without altering the topology. We release the first annotated benchmark for this task across 11 AI subfields. EvoTree attains the highest NMI and citation-direction accuracy among all baselines and the best concept purity on the annotated benchmark, and is the only method with non-trivial marginal-paper detection on the annotated set.

Computation and Language (cs.CL)

arXiv:2609.09561 [cs.CL]

(or arXiv:2609.09561v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.09561 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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