Graph of thoughts: Solving elaborate problems with large language models
Abstract We introduce Graph of Thoughts (GoT): a framework that advances prompting
capabilities in large language models (LLMs) beyond those offered by paradigms such as …
capabilities in large language models (LLMs) beyond those offered by paradigms such as …
High-Performance and Programmable Attentional Graph Neural Networks with Global Tensor Formulations
Graph attention models (A-GNNs), a type of Graph Neural Networks (GNNs), have been
shown to be more powerful than simpler convolutional GNNs (C-GNNs). However, A-GNNs …
shown to be more powerful than simpler convolutional GNNs (C-GNNs). However, A-GNNs …
The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores
Graph databases (GDBs) are crucial in academic and industry applications. The key
challenges in develo** GDBs are achieving high performance, scalability …
challenges in develo** GDBs are achieving high performance, scalability …
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers
Many graph representation learning (GRL) problems are dynamic, with millions of edges
added or removed per second. A fundamental workload in this setting is dynamic link …
added or removed per second. A fundamental workload in this setting is dynamic link …
Neural graph reasoning: Complex logical query answering meets graph databases
Complex logical query answering (CLQA) is a recently emerged task of graph machine
learning that goes beyond simple one-hop link prediction and solves a far more complex …
learning that goes beyond simple one-hop link prediction and solves a far more complex …
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 …
Openfgl: A comprehensive benchmarks for federated graph learning
Federated graph learning (FGL) has emerged as a promising distributed training paradigm
for graph neural networks across multiple local systems without direct data sharing. This …
for graph neural networks across multiple local systems without direct data sharing. This …
Privacy-preserved neural graph databases
In the era of large language models (LLMs), efficient and accurate data retrieval has become
increasingly crucial for the use of domain-specific or private data in the retrieval augmented …
increasingly crucial for the use of domain-specific or private data in the retrieval augmented …
Sparse Hamming Graph: A Customizable Network-on-Chip Topology
Chips with hundreds to thousands of cores require scalable networks-on-chip (NoCs).
Customization of the NoC topology is necessary to reach the diverse design goals of …
Customization of the NoC topology is necessary to reach the diverse design goals of …
Atom: An efficient query serving system for embedding-based knowledge graph reasoning with operator-level batching
Knowledge graph reasoning (KGR) answers logical queries over a knowledge graph (KG),
and embedding-based KGR (EKGR) becomes popular recently, which embeds both queries …
and embedding-based KGR (EKGR) becomes popular recently, which embeds both queries …