LEHAN: Link-Feature Enhanced Heterogeneous Graph Attention Network

Article Computer Science, Artificial Intelligence

Heterogeneous Network Representation Learning: A Unified Framework With Survey and Benchmark

Carl Yang et al.

Summary: This article aims to provide a unified framework for deeply summarizing and evaluating existing research on heterogeneous network embedding (HNE). We first provide a generic paradigm for categorization and analysis of various HNE algorithms, then create four benchmark datasets for fair evaluations, and finally, refactor and amend the implementations of 13 popular HNE algorithms for comprehensive comparisons.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

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Article Physics, Fluids & Plasmas

Link prediction for long-circle-like networks

Ke-ke Shang et al.

Summary: Link prediction is the problem of predicting the relationship between nodes based on observed structural information of a network. Existing algorithms have limitations in predicting certain network structures, but the proposed algorithm in this paper performs well on different network structures.

PHYSICAL REVIEW E (2022)

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Article Computer Science, Artificial Intelligence

A Comprehensive Survey on Graph Neural Networks

Zonghan Wu et al.

Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Review Computer Science, Information Systems

Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

Nurul A. Asif et al.

Summary: This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective, highlighting the success of Graph Neural Networks (GNNs) in solving the problem of processing data in non-euclidean space. Recent developments in computational hardware and optimization have enabled graph networks to learn complex graph relationships and solve various problems effectively.

IEEE ACCESS (2021)

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Review Physics, Multidisciplinary