arXiv:2607. 27303v1 Announce Type: new Abstract: Temporal heterogeneous graphs offer a natural abstraction for dynamic relational systems in which diverse node and relation types co-exist and evolve over time.
arXiv:2609.26855v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) models multi-table databases as heterogeneous temporal graphs, and graph transformers currently achieve state-of-the-a...
arXiv:2604. 17324v2 Announce Type: replace-cross Abstract: Global self-attention drives modern graph transformers, yet the softmax at its core imposes a structural constraint rarely examined directly: every attention row is non-negative and sums to one, so each per-head output is a mass-conserving convex combination of value vectors.
Relational Deep Learning (RDL) models multi-table databases as heterogeneous temporal graphs, and graph transformers currently achieve state-of-the-art performance on benchmarks like RelBench. However...
arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).
The paper introduces FedCORE, a federated adaptation framework for multimodal graph foundation models that jointly optimizes perception (Encoder) and reasoning (GNN) modules via a shared low‑dimensional latent state. Unlike prior methods that freeze the Encoder, FedCORE allows both components to adapt together, addressing the dependency between multimodal evidence extraction and graph‑based relational reasoning. Experiments show that FedCORE significantly narrows the Encoder–GNN pairing gap, achieving an 80.7% reduction compared to independent joint adaptation.
arXiv:2607. 10197v1 Announce Type: new Abstract: Knowledge graph foundation models such as Ultra and Trix achieve strong inductive transfer by learning relation-graph representations that generalise to unseen entities and relations.
FloydNet introduces a learning paradigm that maintains ordered pair states and updates target relations by attending over candidate pairs, inspired by the Floyd–Warshall algorithm. The Pivotal Attention mechanism learns relation composition and pivot weighting in parallel, extending to k-tuples in the ζNet framework. Experiments show ζNet achieves high accuracy on CLRS-30 and near-optimal performance on non-metric TSP instances, matching the discriminative power of k-FWL on BREC.
arXiv:2609.14968v1 Announce Type: new Abstract: Online scheduling of dependency-aware tasks in heterogeneous cloud clusters is a fundamental yet challenging problem due to the complex interplay betwe...
arXiv:2603. 02462v2 Announce Type: replace-cross Abstract: A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen during initial training.
arXiv:2609.38684v1 Announce Type: new Abstract: Knowledge-intensive language-model systems typically represent external knowledge as text chunks or static graphs, with limited support for concept evo...
arXiv:2512. 12477v2 Announce Type: replace Abstract: Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems.
SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.