ACE: Ant Colony Based Multi-Level Network Embedding for Hierarchical Graph Representation Learning

Graph representation learning aims at learning low-dimension representations for nodes in graphs, and has been proven very useful in several downstream tasks. In this article, we propose a new model, Graph Community Infomax (GCI), that can adversarial learn representations for nodes in attributed networks. Different from other adversarial network embedding models, which would assume that the data follow some prior distributions and generate fake examples, GCI utilizes the community information of networks, using nodes as positive(or real) examples and negative(or fake) examples at the same time. An autoencoder is applied to learn the embedding vectors for nodes and reconstruct the adjacency matrix, and a discriminator is used to maximize the mutual information between nodes and communities. Experiments on several real-world and synthetic networks have shown that GCI outperforms various network embedding methods on community detection tasks.

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W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

Intelligent Data Analysis ◽

10.3233/ida-205168 ◽

2021 ◽

Vol 25 (3) ◽

pp. 711-738

Author(s):

Phu Pham ◽

Phuc Do

Keyword(s):

Link Prediction ◽

Representation Learning ◽

Information Network ◽

Network Embedding ◽

Heterogeneous Information Network ◽

Heterogeneous Information ◽

Learning Framework ◽

Novel Approach ◽

Proposed Model ◽

Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning

2020 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm50108.2020.00052 ◽

2020 ◽

Author(s):

Ziyue Qiao ◽

Pengyang Wang ◽

Yanjie Fu ◽

Yi Du ◽

Pengfei Wang ◽

...

Keyword(s):

Metric Learning ◽

Representation Learning ◽

Tree Structure ◽

Graph Representation ◽

Hierarchical Aggregation

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Self-supervised Hierarchical Graph Neural Network for Graph Representation

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9377860 ◽

2020 ◽

Author(s):

Sambaran Bandyopadhyay ◽

Manasvi Aggarwal ◽

M. Narasimha Murty

Keyword(s):

Neural Network ◽

Graph Representation ◽

Hierarchical Graph

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Hyperspectral Image Classification with Localized Graph Convolutional Filtering

Remote Sensing ◽

10.3390/rs13030526 ◽

2021 ◽

Vol 13 (3) ◽

pp. 526

Author(s):

Shengliang Pu ◽

Yuanfeng Wu ◽

Xu Sun ◽

Xiaotong Sun

Keyword(s):

Hyperspectral Image ◽

Principal Component ◽

Representation Learning ◽

Classification Performance ◽

Hyperspectral Data ◽

Spectral Graph Theory ◽

Feature Reduction ◽

Graph Representation ◽

Novel Method ◽

Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

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