arXiv:2602. 19591v3 Announce Type: replace-cross Abstract: Small and Medium Enterprises (SMEs) constitute 99.
arXiv:2606. 24509v1 Announce Type: cross Abstract: Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention.
HeTGB is a new benchmark for heterophilic text‑attributed graphs, consisting of five real‑world datasets where nodes have rich textual descriptions. It allows systematic evaluation of graph neural networks, pre‑trained language models, and co‑training methods on node classification. The benchmark highlights the utility of text attributes, the challenges of heterophilic TAGs, and the limitations of current models.
arXiv:2606. 10249v1 Announce Type: new Abstract: We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs.
arXiv:2607. 12067v1 Announce Type: new Abstract: Institutional equity holdings disclosed in SEC Form 13F filings provide a rich temporal record of portfolio decisions by large investment managers.
arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.
arXiv:2607. 17668v1 Announce Type: cross Abstract: Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph by mitigating cross-domain distribution shifts.
arXiv:2607. 26859v1 Announce Type: cross Abstract: The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data.
HyGRAIL is a framework for discovering scientific hypotheses in incomplete knowledge graphs by combining a graph neural network (GNN) triage with large language model (LLM) review. The GNN scores candidate hypotheses and routes only ambiguous cases to the LLM, which receives structured evidence from the graph converted into natural language. Experiments on MatKG show HyGRAIL achieves the highest F1 score, improves over baselines, and cuts LLM calls by over 54%.
arXiv:2605. 23955v3 Announce Type: replace Abstract: Deploying machine learning in regulated financial environments -- credit risk, fraud detection, and anti-money laundering -- exposes critical vulnerabilities in algorithmic reproducibility.
arXiv:2608.21924v1 Announce Type: new Abstract: Patent litigation imposes substantial costs on firms and distorts R&D incentives, making early risk identification a practically important task. While...
Scientific knowledge graphs organize entities and relations extracted from scientific literature, but they remain inherently incomplete. Missing typed links in such graphs can therefore represent plau...
arXiv:2605.10073v3 Announce Type: replace Abstract: Pre-trained language models advance patent classification and retrieval by encoding claims as flat token sequences, but they overlook the dependenc...