# Inductive Link Prediction for Nodes Having Only Attribute Information

arXiv: https://arxiv.org/abs/2007.08053 · HTML: https://mlsanity.com/paper/2007.08053/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 30 of 30)

- [Knowledge-aware Zero-Shot Learning: Survey and Perspective](https://mlsanity.com/paper/2103.00070/) (2021)
- [Linkless Link Prediction via Relational Distillation](https://mlsanity.com/paper/2210.05801/) (2022)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents](https://mlsanity.com/paper/2112.14731/) (2021)
- [Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2202.00961/) (2022)
- [BScNets: Block Simplicial Complex Neural Networks](https://mlsanity.com/paper/2112.06826/) (2021)
- [Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders](https://mlsanity.com/paper/2108.01053/) (2021)
- [Link Prediction with Non-Contrastive Learning](https://mlsanity.com/paper/2211.14394/) (2022)
- [A Topological Perspective on Demystifying GNN-Based Link Prediction Performance](https://mlsanity.com/paper/2310.04612/) (2023)
- [Sampling Enclosing Subgraphs for Link Prediction](https://mlsanity.com/paper/2206.12004/) (2022)
- [Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching](https://mlsanity.com/paper/2201.11251/) (2022)
- [Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2406.01140/) (2024)
- [IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology](https://mlsanity.com/paper/2106.02926/) (2021)
- [Source-Aware Embedding Training on Heterogeneous Information Networks](https://mlsanity.com/paper/2307.04336/) (2023)
- [Link Prediction with Contextualized Self-Supervision](https://mlsanity.com/paper/2201.10069/) (2022)
- [Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes](https://mlsanity.com/paper/2104.05225/) (2021)
- [New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2211.08972/) (2022)
- [PROXI: Challenging the GNNs for Link Prediction](https://mlsanity.com/paper/2410.01802/) (2024)
- [Real-time and personalized product recommendations for large e-commerce platforms](https://mlsanity.com/paper/2506.21368/) (2025)
- [Understanding and Tackling Over-Dilution in Graph Neural Networks](https://mlsanity.com/paper/2508.16829/) (2025)
- [Inductive learning for product assortment graph completion](https://mlsanity.com/paper/2110.01677/) (2021)
- [Anchor Prediction: A Topic Modeling Approach](https://mlsanity.com/paper/2205.14631/) (2022)
- [Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction](https://mlsanity.com/paper/2307.08877/) (2023)
- [SLJP: Semantic Extraction based Legal Judgment Prediction](https://mlsanity.com/paper/2312.07979/) (2023)
- [CORE: Data Augmentation for Link Prediction via Information Bottleneck](https://mlsanity.com/paper/2404.11032/) (2024)
- [Deep Generative Models for Subgraph Prediction](https://mlsanity.com/paper/2408.04053/) (2024)
- [Leap: Inductive Link Prediction via Learnable TopologyAugmentation](https://mlsanity.com/paper/2503.03331/) (2025)
- [Deep Learning on Graphs for Mobile Network Topology Generation](https://mlsanity.com/paper/2504.13991/) (2025)
- [Meta-learning optimizes predictions of missing links in real-world networks](https://mlsanity.com/paper/2508.09069/) (2025)
- [A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks](https://mlsanity.com/paper/2606.22033/) (2026)

## Abstract (arXiv)

Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which consists of three components: two node embedding encoders and one alignment mechanism. The two encoders aim to output the attribute-oriented node embedding and the structure-oriented node embedding, and the alignment mechanism aligns the two types of embeddings to build the connections between the attributes and links. Our model DEAL is versatile in the sense that it works for both inductive and transductive link prediction. Extensive experiments on several benchmark datasets show that our proposed model significantly outperforms existing inductive link prediction methods, and also outperforms the state-of-the-art methods on transductive link prediction.