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Found 14 papers, 8 papers with code
Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study
PageRank is a widely used approach for measuring the importance of a node in a graph.
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network
Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive computations.
LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs
Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications.
Learning from Graphs with Heterophily: Progress and Future
Graphs are structured data that models complex relations between real-world entities.
Label Propagation for Graph Label Noise
In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes.
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads
LSM-trees are widely adopted as the storage backend of key-value stores.
SIMGA: A Simple and Effective Heterophilous Graph Neural Network with Efficient Global Aggregation
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i. e. neighboring nodes are dissimilar, due to their local and uniform aggregation.
River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning
We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning.
Distributed Graph Embedding with Information-Oriented Random Walks
Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks.
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Recent advances in data processing have stimulated the demand for learning graphs of very large scales.
Proteus: A Self-Designing Range Filter
We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement.
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Further, for other homophilous nodes excluded in the neighborhood, they are ignored for information aggregation.
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Spiking Graph Convolutional Networks
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information.
A Survey on Machine Learning Solutions for Graph Pattern Extraction
Many algorithms are proposed in studying subgraph problems, where one common approach is by extracting the patterns and structures of a given graph.
