# Identity-aware Graph Neural Networks

@inproceedings{You2021IdentityawareGN, title={Identity-aware Graph Neural Networks}, author={Jiaxuan You and Jonathan M. Gomes-Selman and Rex Ying and Jure Leskovec}, booktitle={AAAI}, year={2021} }

Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with…

## 58 Citations

### Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks

- Computer SciencePKDD/ECML Workshops
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This paper introuduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network and shows that there exists high correlation between some of the structural features.

### A N EW P ERSPECTIVE ON "H OW G RAPH N EURAL N ET WORKS G O B EYOND W EISFEILER -L EHMAN ?"

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This work proposes a novel neural model, called GraphSNN, and proves that this model is strictly more expressive than the Weisfeiler Lehman test in distinguishing graph structures, and empirically verify the strength of the model on different graph learning tasks.

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An overview of non-GNN graph embedding methods, which are based on techniques such as random walks, temporal point processes and neural network learning methods, and GNN-based methods which are the application of deep learning on graph data are provided.

### Equivariant Subgraph Aggregation Networks

- Computer ScienceArXiv
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A novel framework to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture, and it is proved that this approach increases the expressive power of both MPNNs and more expressive architectures.

### Revisiting Graph Neural Networks for Link Prediction

- Computer ScienceArXiv
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It is concluded that simply aggregating node embeddings does not lead to effective link representations, while learning from properly labeled subgraphs around links provides highly expressive and generalizable link representations.

### Graph Neural Networks for Link Prediction with Subgraph Sketching

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Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to count triangles…

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### Data Augmentation for Deep Graph Learning: A Survey

- Computer ScienceArXiv
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A taxonomy for graph data augmentation techniques is proposed and a structured review by categorizing the related work based on the augmented information modalities is provided, which points out promising research directions and the challenges in future research.

### Distance and Hop-wise Structures Encoding Enhanced Graph Attention Networks

- Computer ScienceArXiv
- 2021

This work first extracting hop-wise structure information and compute distance distributional information, gathering with node’s intrinsic features, embedding them into same vector space and then adding them up, showing that the DHSEGATs achieve competitive result.

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