SNAP‑KG is a framework that assigns new entities to semantic communities in a growing knowledge graph using only raw features, without graph access or retraining at inference time. The authors evaluate it on five multi‑view benchmarks and a large OGB‑WikiKG2 graph, all of which contain at least one homophilous view, and find strong performance. Extending the evaluation to three heterophilous graphs shows that when no homophilous view exists, clustering quality drops sharply for both SNAP‑KG and transductive baselines; the key factor is the homophily of the relation rather than the number of relations, and multi‑view fusion only helps if at least one homophilous relation is present. "whyItMatters":"The study reveals that SNAP‑KG’s effectiveness relies on the homophily assumption, highlighting a limitation for heterophilous knowledge graphs and suggesting a direction for future research on heterophily‑aware models."
Knowledge graph (KG) construction pipelines must continuously integrate newly arriving entities into a growing graph. Unlike inserting triples between existing nodes, a newly arriving entity has no gr...
SNAP-KG is a framework for integrating newly arriving entities into knowledge graphs by assigning them to semantic communities using a projector that maps raw feature vectors into a learned embedding space. Unlike traditional multi-view graph clustering methods, SNAP-KG supports inductive inference for streaming entities without requiring graph access or model retraining. Experiments on five benchmark datasets and a large-scale KG show significant inference speedups and competitive clustering quality, while reducing candidate search for entity resolution and link prediction by up to 97%.
FedV-KGQA addresses multi‑hop question answering over vertically partitioned knowledge graphs where each silo holds disjoint relation types. The system trains local embeddings, concatenates silo‑specific entity views, anchors questions at a topic entity, and ranks candidates without sharing raw triples. Experiments show federated fusion nearly matches centralized accuracy, that anchoring and enrichment are more critical than embedding choice, and that the cheapest encoder depends on target accuracy.
arXiv:2606. 18001v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models (KGFMs) are zero-shot generalizers: trained once, they can predict links on unseen graphs without retraining.
arXiv:2606. 27967v1 Announce Type: new Abstract: Real-world knowledge graphs are often incomplete, lacking many valid facts.
The paper introduces GONE, a benchmark for evaluating knowledge unlearning in large language models using structured knowledge graphs, and presents Neighborhood-Expanded Distribution Shaping (NEDS), a framework that leverages graph connectivity to separate forgotten facts from their semantic neighborhood. GONE disentangles direct fact removal, reasoning-based leakage, and catastrophic forgetting, while NEDS achieves high unlearning efficacy and locality on LLaMA-3-8B and Mistral-7B. The dataset is publicly available on Hugging Face.
FedV-KGQA is a framework for multi-hop question answering over knowledge graphs that are vertically partitioned across different organizations. It allows entities to be shared while each silo retains disjoint sets of relations, using local graph enrichment and knowledge graph embeddings so that raw triples and relation parameters never leave the silo. The system includes a topic entity anchoring mechanism to ground questions in the correct graph neighborhood without runtime inter-silo communication, and it achieves performance close to centralized systems on three benchmarks, including 3-hop reasoning and robustness to embedding perturbations.
arXiv:2609.13664v1 Announce Type: new Abstract: Knowledge graphs are increasingly distributed across autonomous organizations that share an entity space but own disjoint subsets of relations, forming...
arXiv:2609. 03487v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) demonstrates its effectiveness for predicting missing links in knowledge graphs (KGs) by projecting entities and relations into a low-dimensional vector space.
arXiv:2608. 06402v1 Announce Type: new Abstract: Community detection is a fundamental task in graph analytics that aims to identify cohesive groups of entities with similar behaviors or interests.
arXiv:2603. 29875v3 Announce Type: replace-cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector.
arXiv:2606. 29180v1 Announce Type: new Abstract: A Knowledge Graph (KG) represents facts as structured triples and is widely used to organize relational knowledge across diverse domains.