arXiv Machine Learning

Language-encoded network topology enables large language models to reason about complex networks

The paper introduces BioGlyph, a method that translates network topology into a language of structural roles such as hubs, community cores, and cross-community connectors. By encoding these roles in a universal, interpretable vocabulary, BioGlyph allows large language models to answer structural reasoning questions about networks more accurately—improving system accuracy by up to 26 percentage points compared to edge-based or numerical representations. Experiments across twenty networks in five domains show the approach is especially effective for dense, community-structured networks and reveals biologically meaningful patterns in a budding-yeast protein-interaction network.

arXiv AI

Sep 3

Unifying biomedical knowledge in a modern multimodal graph

OptimusKG is a multimodal biomedical labeled property graph that integrates structured and semi‑structured resources to preserve detailed, type‑specific metadata across molecular, anatomical, clinical, and environmental domains. The graph contains nearly 191,000 nodes, over 21.8 million edges, and more than 67 million property instances derived from 18 ontologies, with a top‑level schema that enforces node and edge constraints while retaining granular provenance. Validation using the PaperQA3 agent found that 70.0% of sampled edges are supported by literature evidence, and the graph offers a standardized resource for machine learning, knowledge‑grounded retrieval, and hypothesis generation in biomedical research.

By Lucas Vittor, Ayush Noori, I\~naki Arango, Joaqu\'in Polonuer, Sam Rodriques, Andrew White, David A. Clifton, Marinka Zitnik

llms agents multimodal

arXiv AI

Aug 10

Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes

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.

By Aoting Zeng, Kai Wang, Jianwei Wang, Yuxiang Sun, Yizhang He, Wenjie Zhang

llms benchmarks safety

arXiv AI

Jul 1

Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression

arXiv:2601. 08187v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising capabilities in Text-Attributed Graph (TAG) understanding.

By Zijun Di, Bin Lu, Huquan Kang, Luoyi Fu, Jiaxin Ding, Xiaoying Gan, Lei Zhou, Xinbing Wang

llms benchmarks

arXiv Machine Learning

Jun 29

Effects of relational graph modularity and depth on the learning performance of neural networks

arXiv:2507. 10005v2 Announce Type: replace Abstract: In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention.

By Yash Arya, Sang Hoon Lee

computer-vision reinforcement-learning

arXiv Computation and Language

Aug 24

Do Large Language Models Play Six Degrees of Separation? Measuring Topological Compression in Long-Context Manifolds

The paper shows that large language models (LLMs) naturally organize their hidden state manifolds into small‑world networks, enabling efficient multi‑hop reasoning. By converting similarity matrices into unweighted graphs, the authors trace connectivity between distant semantic anchors and find a sharp topological phase transition: deep reasoning layers compress conceptual distances into paths bounded by six semantic hops, while early syntactic layers remain fragmented. The framework is applied to zero‑shot hallucination detection in Retrieval‑Augmented Generation, revealing that factual generations preserve a ~3‑hop structure, whereas hallucinations collapse the topology.

By Md. Faiyaz Abdullah Sayeedi

llms rag safety

arXiv Machine Learning

Aug 7

MetaboLLM: a metabolomics-specialized large language model for biochemical knowledge integration and predictive metabolite graph construction

arXiv:2608. 06253v1 Announce Type: new Abstract: Metabolomics knowledge is distributed across heterogeneous resources and remains difficult to translate into predictive representations.

By Dohyun Ku, Min Gu Kwak, Francisco J. Pasquel, Jing Li

llms fine-tuning benchmarks

arXiv AI

Jul 3

MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.

By Wenbo Zhang

rag multimodal benchmarks

arXiv AI

Jul 8

Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy

arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.

By Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu

benchmarks

arXiv AI

Sep 10

DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing

arXiv:2609.06779v1 Announce Type: cross Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cos...

By Zijie Liu, Hongxuan Li, Zhen Tan, Jinhao Duan, Baixiang Huang, Zunpeng Liu, Kai Shu, Tianlong Chen

llms efficiency safety

arXiv AI

Jun 9

Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling

arXiv:2606. 07686v1 Announce Type: cross Abstract: Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods.

By Ravisha Rupasinghe, Rajith Vidanaarachchi, Asela Hevapathige, Sachith Seneviratne, Sen-Lin Tang, Saman Halgamuge

benchmarks

arXiv Machine Learning

Jun 3

When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models

arXiv:2606. 03712v1 Announce Type: new Abstract: Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks.

By Ding Zhang, Runtao Zhou, Wenqing Zheng, Rizal Fathony, Bayan Bruss, Chirag Agarwal

llms efficiency multimodal safety

arXiv Machine Learning

Jun 30

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks

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.

By Haoxin Sun, Yiqing Lin, Yajun Huang, Chenhui Dong, Mingjun Li, Zhongzhi Zhang

llms diffusion fine-tuning benchmarks safety

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