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 …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
Parallel and distributed graph neural networks: An in-depth concurrency analysis
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …
routinely solve complex problems on unstructured networks, such as node classification …
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 developing GDBs are achieving high performance, scalability …
challenges in developing GDBs are achieving high performance, scalability …
Motif prediction with graph neural networks
M Besta, R Grob, C Miglioli, N Bernold… - Proceedings of the 28th …, 2022 - dl.acm.org
Link prediction is one of the central problems in graph mining. However, recent studies
highlight the importance of higher-order network analysis, where complex structures called …
highlight the importance of higher-order network analysis, where complex structures called …
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers
M Besta, AC Catarino, L Gianinazzi… - Learning on Graphs …, 2024 - proceedings.mlr.press
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 databases
Graph databases (GDBs) enable processing and analysis of unstructured, complex, rich,
and usually vast graph datasets. Despite the large significance of GDBs in both academia …
and usually vast graph datasets. Despite the large significance of GDBs in both academia …
Probgraph: High-performance and high-accuracy graph mining with probabilistic set representations
Important graph mining problems such as Clustering are computationally demanding. To
significantly accelerate these problems, we propose ProbGraph: a graph representation that …
significantly accelerate these problems, we propose ProbGraph: a graph representation that …
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 …