Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
arXiv:2012.10024
Abstract
A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types. We study the problem of classifying objects in HINs. Most existing methods perform poorly when given scarce labeled objects as training sets, and methods that improve classification accuracy under such scenarios are often computationally expensive. To address these problems, we propose ConCH, a graph neural network model. ConCH formulates the classification problem as a multi-task learning problem that combines semi-supervised learning with self-supervised learning to learn from both labeled and unlabeled data. ConCH employs meta-paths, which are sequences of object types that capture semantic relationships between objects. ConCH co-derives object embeddings and context embeddings via graph convolution. It also uses the attention mechanism to fuse such embeddings. We conduct extensive experiments to evaluate the performance of ConCH against other 15 classification methods. Our results show that ConCH is an effective and efficient method for HIN classification.
Accepted by ICDE 2021
References in corpus (14)
- Distributed Representations of Words and Phrases and their Compositionality
- A Simple Framework for Contrastive Learning of Visual Representations
- MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
- Graph Transformer Networks
- Representation Learning for Attributed Multiplex Heterogeneous Network
- Attributed Social Network Embedding
- Contrastive Multi-View Representation Learning on Graphs
- InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
- Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
- Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
- Heterogeneous Deep Graph Infomax
- When Does Self-Supervision Help Graph Convolutional Networks?
- Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
- Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels