# Graph Regularized Transductive Classification on Heterogeneous Information Networks

@inproceedings{Ji2010GraphRT, title={Graph Regularized Transductive Classification on Heterogeneous Information Networks}, author={Ming Ji and Yizhou Sun and Marina Danilevsky and Jiawei Han and Jing Gao}, booktitle={ECML/PKDD}, year={2010} }

A heterogeneous information network is a network composed of multiple types of objects and links. Recently, it has been recognized that strongly-typed heterogeneous information networks are prevalent in the real world. Sometimes, label information is available for some objects. Learning from such labeled and unlabeled data via transductive classification can lead to good knowledge extraction of the hidden network structure. However, although classification on homogeneous networks has been…

## 228 Citations

### Transductive Classification on Heterogeneous Information Networks with Edge Betweenness-based Normalization

- Computer ScienceWSDM
- 2016

A novel method for transductive classification on heterogeneous information networks composed of multiple types of vertices, based on the intuition that edges bridging across communities are less trustworthy, which is more effective than a state-of-the-art method, GNetMine.

### Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks

- Computer ScienceACM Trans. Knowl. Discov. Data
- 2018

A transductive approach to classification that learns to project the different types of nodes into a common latent space, and embedding is learned so as to reflect different characteristics of the problem such as the correlation between node labels, as well as the graph topology.

### HeteClass: A Meta-path based framework for transductive classification of objects in heterogeneous information networks

- Computer ScienceExpert Syst. Appl.
- 2017

### A Tensor-Based Markov Chain Model for Heterogeneous Information Network Collective Classification

- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2022

A Tensor-based Markov chain (T-Mark) approach is proposed, which is able to automatically and simultaneously predict the labels for unlabeled nodes and give the relative importance of types of links that actually improve the classification accuracy.

### HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks

- Computer ScienceECIR
- 2014

This paper uses the concept of meta path to represent the different relation paths in heterogeneous networks and proposes a novel meta path selection model, named HetPathMine, which can get higher accuracy than the existing transductive classification methods and the weight obtained for each meta path is consistent with human intuition or real-world situations.

### Robust Classification of Information Networks by Consistent Graph Learning

- Computer ScienceECML/PKDD
- 2015

This work proposes a novel algorithm, namely Consistent Graph Learning, which is robust to the inconsistent links of a network and outperforms the state-of-the-art methods on both homogeneous and heterogeneous network datasets.

### Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks

- Computer Science2021 IEEE 37th International Conference on Data Engineering (ICDE)
- 2021

This work proposes ConCH, a graph neural network model that formulates the classification problem as a multitask learning problem that combines semi-supervised learning with self-super supervised learning to learn from both labeled and unlabeled data.

### A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification

- Computer ScienceSAC
- 2014

This paper proposes a parameter-free algorithm for transductive classification of textual data, referred to as LPBHN (Label Propagation using Bipartite Heterogeneous Networks), which uses a bipartite heterogeneous network to perform the classification task.

### GraphInception: Convolutional Neural Networks for Collective Classification in Heterogeneous Information Networks

- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2021

A deep convolutional collective classification method, called GraphInception, is proposed, to learn the deep relational features in HINs, which involve different types of autocorrelations, from simple to complex relations, among the instances.

### HRank: A Path based Ranking Framework in Heterogeneous Information Network

- Computer ScienceArXiv
- 2014

This paper studies the ranking problem in heterogeneous networks and proposes the HRank framework to evaluate the importance of multiple types of objects and meta paths and proposes a constrained meta path to subtly capture the rich semantics in heterogeneity networks.

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