Reasoning on graphs: Faithful and interpretable large language model reasoning
Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
Structure-free graph condensation: From large-scale graphs to condensed graph-free data
Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …
scale condensed graph as its substitution, has immediate benefits for various graph learning …
G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
Given a graph with textual attributes, we enable users tochat with their graph': that is, to ask
questions about the graph using a conversational interface. In response to a user's …
questions about the graph using a conversational interface. In response to a user's …
[HTML][HTML] Graph spatiotemporal process for multivariate time series anomaly detection with missing values
The detection of anomalies in multivariate time series data is crucial for various practical
applications, including smart power grids, traffic flow forecasting, and industrial process …
applications, including smart power grids, traffic flow forecasting, and industrial process …
Topologies of reasoning: Demystifying chains, trees, and graphs of thoughts
M Besta, F Memedi, Z Zhang, R Gerstenberger… - arXiv preprint arXiv …, 2024 - arxiv.org
The field of natural language processing (NLP) has witnessed significant progress in recent
years, with a notable focus on improving large language models'(LLM) performance through …
years, with a notable focus on improving large language models'(LLM) performance through …
MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion
Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-
dependent properties and the evolving nature of knowledge over time. TKGs typically …
dependent properties and the evolving nature of knowledge over time. TKGs typically …
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
Knowledge graphs (KGs) store enormous facts as relationships between entities. Due to the
long-tailed distribution of relations and the incompleteness of KGs, there is growing interest …
long-tailed distribution of relations and the incompleteness of KGs, there is growing interest …
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
Graph neural networks (GNNs) have gained significant attraction due to their expansive real-
world applications. To build trustworthy GNNs, two aspects-fairness and privacy-have …
world applications. To build trustworthy GNNs, two aspects-fairness and privacy-have …
KG-RAG: Bridging the Gap Between Knowledge and Creativity
D Sanmartin - arXiv preprint arXiv:2405.12035, 2024 - arxiv.org
Ensuring factual accuracy while maintaining the creative capabilities of Large Language
Model Agents (LMAs) poses significant challenges in the development of intelligent agent …
Model Agents (LMAs) poses significant challenges in the development of intelligent agent …
Microstructures and Accuracy of Graph Recall by Large Language Models
Graphs data is crucial for many applications, and much of it exists in the relations described
in textual format. As a result, being able to accurately recall and encode a graph described …
in textual format. As a result, being able to accurately recall and encode a graph described …