SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation

2024-01-07 · Ming-Yi Hong, Yi-Hsiang Huang, Shao-En Lin, You-Chen Teng, Chih-Yu Wang, Che Lin

Graph Neural Networks (GNNs) excel in delineating graph structures in diverse domains, including community analysis and recommendation systems. As the interpretation of GNNs becomes increasingly important, the demand for robust baselines and expansive graph datasets is accentuated, particularly in the context of Heterogeneous Information Networks (HIN). Addressing this, we introduce SynHING, a novel framework for Synthetic Heterogeneous Information Network Generation aimed at enhancing graph learning and explanation. SynHING systematically identifies major motifs in a target HIN and employs a bottom-up generation process with intra-cluster and inter-cluster merge modules. This process, supplemented by post-pruning techniques, ensures the synthetic HIN closely mirrors the original graph's structural and statistical properties. Crucially, SynHING provides ground-truth motifs for evaluating GNN explainer models, setting a new standard for explainable, synthetic HIN generation and contributing to the advancement of interpretable machine learning in complex networks.

📄 PDF Abstract BibTeX arXiv:2401.04133

Code (0)

등록된 구현이 없습니다.

Tasks

Graph Learning Interpretable Machine Learning Node Classification Recommendation Systems

Methods 이 논문이 사용한 방법론

Graph Neural Network 설명 없음

ALIGN In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss…

PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes

2025-07-28 · Tianhao Wang, Simon Klancher, Kunal Mukherjee, Josh Wiedemeier 외 arxiv

The rise of graph-structured data has driven interest in graph learning and synthetic data generation. While successful in text and image domains, synthetic graph generation remains challenging -- especially for real-wor…

Synthetic Data Generation Graph Generation Knowledge Graphs Graph Learning

MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation

2023-10-30 · Chandrani Kumari, Rahul Siddharthan

We provide new algorithms for two tasks relating to heterogeneous tabular datasets: clustering, and synthetic data generation. Tabular datasets typically consist of heterogeneous data types (numerical, ordinal, categoric…

Clustering Synthetic Data Generation Tabular Data Generation

Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation

2026-06-12 · Francesco Cazzaro, Jessica Lennon, Ariadna Quattoni arxiv

Property Graphs are rapidly being adopted as database frameworks for representing heterogeneous data sources. To enable precise access to the information contained in them we need conversational interfaces based on Text-…

Synthetic Data Generation

A GAN Approach for Node Embedding in Heterogeneous Graphs Using Subgraph Sampling

2023-12-11 · Hung-Chun Hsu, Bo-Jun Wu, Ming-Yi Hong, Che Lin 외

Graph neural networks (GNNs) face significant challenges with class imbalance, leading to biased inference results. To address this issue in heterogeneous graphs, we propose a novel framework that combines Graph Neural N…

Generative Adversarial Network Graph Neural Network

Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs

2021-05-03 · Saurav Manchanda, Da Zheng, George Karypis

Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. GCNs incorporate the graph structure information and the node (or edge) features throu…