# Advanced Graph Neural Networks

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/advanced-graph-neural-networks/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the development, applications, and techniques related to Graph Neural Networks (GNNs) and their variants. It covers topics such as knowledge graph embedding, representation learning, network embedding, deep learning, graph convolutional networks, heterogeneous networks, relational data modeling, signal processing on graphs, and semi-supervised learning. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11273 |
| Works | 51 |

## Topic papers all

Showing 15 of 51.

- [Graph neural networks: A review of methods and applications](https://scholariq.org/papers/graph-neural-networks-a-review-of-methods-and-applications-2/)
- [Graph Neural Networks: A Review of Methods and Applications](https://scholariq.org/papers/graph-neural-networks-a-review-of-methods-and-applications/)
- [Graph Contextualized Self-Attention Network for Session-based Recommendation](https://scholariq.org/papers/graph-contextualized-self-attention-network-for-session-based-recommendation/)
- [Are Graph Augmentations Necessary?](https://scholariq.org/papers/are-graph-augmentations-necessary/)
- [A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions](https://scholariq.org/papers/a-review-of-graph-neural-networks-concepts-architectures-techniques-challenges/)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://scholariq.org/papers/self-supervised-multi-channel-hypergraph-convolutional-network-for-social/)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://scholariq.org/papers/anomaly-detection-on-attributed-networks-via-contrastive-self-supervised/)
- [MGAE](https://scholariq.org/papers/mgae/)
- [Feature-level Deeper Self-Attention Network for Sequential Recommendation](https://scholariq.org/papers/feature-level-deeper-self-attention-network-for-sequential-recommendation/)
- [Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach](https://scholariq.org/papers/knowledge-transfer-for-out-of-knowledge-base-entities-a-graph-neural-network/)
- [Graph-based deep learning for communication networks: A survey](https://scholariq.org/papers/graph-based-deep-learning-for-communication-networks-a-survey/)
- [XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation](https://scholariq.org/papers/xsimgcl-towards-extremely-simple-graph-contrastive-learning-for-recommendation/)
- [Factorized graph matching](https://scholariq.org/papers/factorized-graph-matching/)
- [Are we really making much progress?](https://scholariq.org/papers/are-we-really-making-much-progress/)
- [Socially-Aware Self-Supervised Tri-Training for Recommendation](https://scholariq.org/papers/socially-aware-self-supervised-tri-training-for-recommendation/)

## Topic primary papers

Showing 15 of 29.

- [Graph neural networks: A review of methods and applications](https://scholariq.org/papers/graph-neural-networks-a-review-of-methods-and-applications-2/)
- [Graph Neural Networks: A Review of Methods and Applications](https://scholariq.org/papers/graph-neural-networks-a-review-of-methods-and-applications/)
- [A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions](https://scholariq.org/papers/a-review-of-graph-neural-networks-concepts-architectures-techniques-challenges/)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://scholariq.org/papers/anomaly-detection-on-attributed-networks-via-contrastive-self-supervised/)
- [MGAE](https://scholariq.org/papers/mgae/)
- [Graph-based deep learning for communication networks: A survey](https://scholariq.org/papers/graph-based-deep-learning-for-communication-networks-a-survey/)
- [Are we really making much progress?](https://scholariq.org/papers/are-we-really-making-much-progress/)
- [PME](https://scholariq.org/papers/pme/)
- [ProNE: Fast and Scalable Network Representation Learning](https://scholariq.org/papers/prone-fast-and-scalable-network-representation-learning/)
- [Understanding Negative Sampling in Graph Representation Learning](https://scholariq.org/papers/understanding-negative-sampling-in-graph-representation-learning/)
- [Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning](https://scholariq.org/papers/multi-scale-contrastive-siamese-networks-for-self-supervised-graph/)
- [Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering](https://scholariq.org/papers/rethinking-graph-auto-encoder-models-for-attributed-graph-clustering/)
- [Complex graph convolutional network for link prediction in knowledge graphs](https://scholariq.org/papers/complex-graph-convolutional-network-for-link-prediction-in-knowledge-graphs/)
- [GraphFormers: GNN-nested Transformers for Representation Learning on\n Textual Graph](https://scholariq.org/papers/graphformers-gnn-nested-transformers-for-representation-learning-on-n-textual/)
- [Type-augmented Relation Prediction in Knowledge Graphs](https://scholariq.org/papers/type-augmented-relation-prediction-in-knowledge-graphs/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.