Graph Neural Networks for Real-World Complex Scenarios

This article presents a comprehensive overview of recent graph neural network research, covering adversarial representation learning for network embedding, block‑model guided GCN, enhanced class‑discriminative GNNs, self‑supervised contrastive GNNs, experimental results, and conclusions, highlighting their significance in real‑world applications.

GCNadversarial learninggraph neural networks

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Graph Neural Networks for Real-World Complex Scenarios

Alibaba Cloud Developer

Aug 16, 2019 · Artificial Intelligence

How IntentGC Scales Graph Convolution for Billion‑Node Recommendation Systems

IntentGC, a KDD 2019 paper, introduces a scalable graph convolution framework that fuses explicit user‑item interactions with rich heterogeneous signals to tackle link‑prediction on billion‑node e‑commerce graphs, offering efficient training, dual‑convolution design, and superior performance over existing baselines.

IntentGCRecommendation Systemsgraph convolution

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How IntentGC Scales Graph Convolution for Billion‑Node Recommendation Systems

AntTech

Dec 20, 2017 · Artificial Intelligence

Network Embedding Overview and Recent Research Directions from CIKM 2017

An overview of network embedding presented at CIKM 2017, covering its definition, loss functions, algorithm categories such as spectral methods, random walks, deep learning models, emerging research topics like dynamic and attributed embeddings, and various application scenarios illustrated with numerous academic papers.

CIKM2017Deep Learningattribute integration

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Network Embedding Overview and Recent Research Directions from CIKM 2017