Time-Aligned Evolving Concept Graphs for Scientific Relation Forecasting

arXiv:2609.18163v1 Announce Type: new Abstract: Forecasting scientific relations can guide discovery by identifying promising connections before they emerge. Existing approaches often model concept semantics and graph structure separately or summarize semantics over coarse historical snapshots, leaving semantic representations potentially misaligned with rapidly evolving graph evidence. We propose a time-aligned evolving concept graph framework that jointly models semantic and structural evolution. Its core idea is to treat dated papers as shared update events, reconstructing semantic and structural states from the same publication history through each prediction time. Pair-level fusion combines these states to forecast first co-occurrence, relation formation, and conditional relation type. Holding architecture and training fixed, refreshing context alongside graph updates improves mean relation AUPRC by 16.6% over frozen context. On a graph built from 187,848 papers with 270,687 concepts and 7.45 million co-occurrence links, the complete framework improves mean relation AUROC from 0.9290 for the strongest evaluated baseline to 0.9722, with mean population-weighted AUPRC 0.005778.

Related

• Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
• Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effects
• Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs

Source: arXiv cs.AI | 2026-09-17