On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “Graph-BERT”

Graph-Bert: Only Attention is Needed for Learning Graph Representationscs.LG

GRAPH-BERT uses only attention for graph representation learning.

problem Graph neural networks over-rely on graph links and suffer from performance issues.

method GRAPH-BERT uses only attention mechanism without graph convolution or aggregation, trained on sampled subgraphs.

result GRAPH-BERT outperforms existing GNNs in learning effectiveness and efficiency.

Graph Neural Networks Attention Mechanism Graph Representation Learning Effectiveness Efficiency

2020-01-15abs ↗pdf ↗

Segmented Graph-Bert for Graph Instance Modelingcs.LG

SEG-BERT improves graph instance learning by adapting GRAPH-BERT.

problem Graph instance representation learning challenges due to diverse sizes and node order.

method Adapted GRAPH-BERT with a segmented architecture to handle graph node orderlessness and diverse sizes.

result SEG-BERT outperforms comparison methods on six out of seven benchmark datasets.

Graph instance learning GRAPH-BERT Segmented GRAPH-BERT Node orderlessness Graph node size

2020-02-09abs ↗pdf ↗

G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learningcs.LG

G5 universal GRAPH-BERT learns graph representations across different datasets.

problem Learning graph representations across diverse graph datasets with distinct input and output configurations.

method G5 introduces a pluggable model architecture with input and output components for each graph data source, connected via a unified layer and fusion layer.

result G5 removes obstacles for cross-graph representation learning and transfer, even for sparse data.

GRAPH-BERT graph representation learning graph-to-graph transfer Apocalypse Learning cross-source classification

2020-06-11abs ↗pdf ↗

Graph Neural Distance Metric Learning with Graph-Bertcs.LG

Graph neural network learns graph distances effectively.

problem Maintaining graph distance metric properties.

method GRAPH-BERT based semi-supervised distance metric learning.

result GB-DISTANCE outperforms existing methods.

Graph Neural Networks Distance Metrics Graph Learning Graph-BERT Semi-Supervised Learning

2020-02-09abs ↗pdf ↗