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Found 15 papers, 4 papers with code
Demystifying Chains, Trees, and Graphs of Thoughts
Among these, prompt engineering coupled with structures has emerged as a promising paradigm, with designs such as Chain-of-Thought, Tree of Thoughts, or Graph of Thoughts, in which the overall LLM reasoning is guided by a structure such as a graph.
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers
A fundamental workload in this setting is dynamic link prediction: using a history of graph updates to predict whether a given pair of vertices will become connected.
Cached Operator Reordering: A Unified View for Fast GNN Training
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering.
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT).
Neural Graph Databases
In general, LPG2vec enables combining predictive power of the most powerful GNNs with the full scope of information encoded in the LPG model, paving the way for neural graph databases, a class of systems where the vast complexity of maintained data will benefit from modern and future graph machine learning methods.
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis
To alleviate this, we first design a taxonomy of parallelism in GNNs, considering data and model parallelism, and different forms of pipelining.
Learning Combinatorial Node Labeling Algorithms
We present a novel neural architecture to solve graph optimization problems where the solution consists of arbitrary node labels, allowing us to solve hard problems like graph coloring.
Motif Prediction with Graph Neural Networks
We also successfully apply our architecture for predicting more arbitrary clusters and communities, illustrating its potential for graph mining beyond motif analysis.
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms.
Log(Graph): A Near-Optimal High-Performance Graph Representation
Our high-performance Log(Graph) implementation based on modern bitwise operations and state-of-the-art succinct data structures achieves high compression ratios as well as performance.
Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems
We also crystallize the meaning of different concepts associated with streaming graph processing, such as dynamic, temporal, online, and time-evolving graphs, edge-centric processing, models for the maintenance of updates, and graph databases.
Distributed, Parallel, and Cluster Computing Databases Data Structures and Algorithms Performance
Red-blue pebbling revisited: near optimal parallel matrix-matrix multiplication
The key idea behind COSMA is to derive an optimal (up to a factor of 0. 03\% for 10MB of fast memory) sequential schedule and then parallelize it, preserving I/O optimality.
Computational Complexity Distributed, Parallel, and Cluster Computing Performance
Graph Processing on FPGAs: Taxonomy, Survey, Challenges
To facilitate understanding of this emerging domain, we present the first survey and taxonomy on graph computations on FPGAs.
Distributed, Parallel, and Cluster Computing Hardware Architecture
A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning
We introduce Deep500: the first customizable benchmarking infrastructure that enables fair comparison of the plethora of deep learning frameworks, algorithms, libraries, and techniques.
Scaling betweenness centrality using communication-efficient sparse matrix multiplication
Betweenness centrality (BC) is a crucial graph problem that measures the significance of a vertex by the number of shortest paths leading through it.
Distributed, Parallel, and Cluster Computing Discrete Mathematics Mathematical Software G.1.0; G.2.2
