Abstract: Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for nonEuclidean data on graphs, which often require a large amount of labeled data with high cost.

Agile rituals are great, but without tracking constraints and dependencies live, your transformation will stall.

A professionally curated list of awesome resources (paper, code, data, etc.) on Deep Graph Anomaly Detection (DGAD), which is the first work to comprehensively and systematically summarize the recent ...

This code was tested with PyTorch 2.0.1, cuda 11.8 and torch_geometrics 2.3.1. Note that ${PROJECT_DIR} refers to this directory. The following section outlines the ...

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Abstract: In the practical use of graph databases, storing graphs separately enhances maintainability, while integrating them into a unified graph facilitates advanced analytics. To address these dual ...