Hugging Face Trending Papers
Time-Aligned Evolving Concept Graphs for Scientific Relation Forecasting
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arXiv AI
Sep 17
Time-Aligned Evolving Concept Graphs for Scientific Relation Forecasting
The paper introduces a time‑aligned evolving concept graph framework that jointly models semantic and structural changes in scientific literature. By treating dated papers as shared update events, it reconstructs both semantic and structural states from the same publication history for each prediction time, and fuses these states at the pair level to forecast co‑occurrence, relation formation, and conditional relation type. Experiments on a large graph of 187,848 papers and 270,687 concepts show that refreshing context with graph updates boosts mean relation AUPRC by 16.6% and raises mean relation AUROC from 0.9290 to 0.9722.
arXiv Machine Learning
Sep 22
SCoR: A Hierarchical Framework for Forecasting Relations Between Scientific Concepts
arXiv:2609.22174v1 Announce Type: cross Abstract: Anticipating emerging research directions is a critical goal of AI-assisted science. Existing methods mainly predict which concepts will co-occur in...
arXiv Machine Learning
Jul 13
Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation
arXiv:2607. 09232v1 Announce Type: new Abstract: Temporal knowledge graphs (TKGs) represent evolving relational systems, whose underlying data-generating processes often change over time.
arXiv Machine Learning
Oct 15
Dynamic Graph World Modelling: Learning Future Dynamics from Temporal Context
arXiv:2610.19364v1 Announce Type: new Abstract: Dynamic graph learning models how entities and their relations evolve from historical observations to predict future graph states. Existing methods do...
arXiv Computation and Language
Sep 10
Towards Automatic Evolution Tree Generation from Citation Graphs
The paper introduces EvoTree, a staged framework for automatically generating evolution trees from citation graphs. It separates backbone learning from temporal refinement, using a graph-aware encoder and hierarchical clustering to build a stable taxonomy, then fine-tunes temporally to attach marginal papers under monotonic-path constraints, and finally labels concepts with an LLM without changing the topology. The authors release an annotated benchmark across 11 AI subfields and report that EvoTree outperforms baselines in NMI, citation-direction accuracy, concept purity, and marginal-paper detection.
arXiv Machine Learning
Jul 7
Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs
arXiv:2607. 02872v1 Announce Type: new Abstract: Dynamic knowledge graphs are ubiquitous in today's AI applications, as we represent molecular structures, social relationships, and language information using these graph models.