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archi829/
heterogat-v2

Name: heterogat-v2
Availability: InStock

pytorch

graph-neural-networks

heterogat

anomaly-detection

username/bottleneck-oracle-data

mit

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HeteroGAT v2 — Bottleneck Oracle Model

Overview

HeteroGAT v2 is a heterogeneous graph attention network designed to detect bottlenecks and anomalous patterns in system trace graphs.

It operates on graph-structured data derived from execution traces and learns node/edge relationships to identify suspicious or high-impact components.

Model Details

Architecture: Heterogeneous Graph Attention Network (HeteroGAT)
Framework: PyTorch + PyTorch Geometric
Model File: heterogat_v2.pt

Key Components

Multi-head attention on heterogeneous node types
Learned embeddings for different entity types
Graph-level aggregation for prediction

Dataset

Trained on: 👉 https://huggingface.co/datasets/archi829/bottleneck-oracle-data

Input

Preprocessed graph objects (graphs_v2.pkl)
Nodes represent system entities (services, processes, etc.)
Edges represent interactions/dependencies

archi829/heterogat-v2