# Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

arXiv: https://arxiv.org/abs/2006.01963 · HTML: https://mlsanity.com/paper/2006.01963/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

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

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [All in One: Multi-task Prompting for Graph Neural Networks](https://mlsanity.com/paper/2307.01504/) (2023)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation](https://mlsanity.com/paper/2212.12738/) (2022)
- [Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks](https://mlsanity.com/paper/2102.06371/) (2021)
- [Decentralized Collaborative Learning Framework for Next POI Recommendation](https://mlsanity.com/paper/2204.06516/) (2022)
- [TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature](https://mlsanity.com/paper/2205.12784/) (2022)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution](https://mlsanity.com/paper/2207.00594/) (2022)
- [WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning](https://mlsanity.com/paper/2212.14182/) (2022)
- [Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations](https://mlsanity.com/paper/2204.00327/) (2022)
- [Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2206.08088/) (2022)
- [A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation](https://mlsanity.com/paper/2112.02275/) (2021)
- [QDC: Quantum Diffusion Convolution Kernels on Graphs](https://mlsanity.com/paper/2307.11234/) (2023)
- [Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules](https://mlsanity.com/paper/2308.11034/) (2023)
- [SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching](https://mlsanity.com/paper/2205.13679/) (2022)
- [Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework](https://mlsanity.com/paper/2301.04318/) (2023)
- [All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)](https://mlsanity.com/paper/2403.07040/) (2024)
- [Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors](https://mlsanity.com/paper/2111.11335/) (2021)
- [Spatial-Temporal Meta-path Guided Explainable Crime Prediction](https://mlsanity.com/paper/2205.01901/) (2022)
- [RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space](https://mlsanity.com/paper/2403.01864/) (2024)
- [Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes](https://mlsanity.com/paper/2412.09947/) (2024)
- [Domain Adaptive Graph Classification](https://mlsanity.com/paper/2312.13536/) (2023)
- [G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs](https://mlsanity.com/paper/2606.01873/) (2026)
- [Incentive Compatible Pareto Alignment for Multi-Source Large Graphs](https://mlsanity.com/paper/2112.02792/) (2021)
- [Hyperedge Interaction-aware Hypergraph Neural Network](https://mlsanity.com/paper/2401.15587/) (2024)
- [DP-GPL: Differentially Private Graph Prompt Learning](https://mlsanity.com/paper/2503.10544/) (2025)
- [Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data](https://mlsanity.com/paper/2504.01979/) (2025)
- [Chaining 2-FWL GNNs for Combinatorial Graph Alignment](https://mlsanity.com/paper/2510.03086/) (2025)
- [S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs](https://mlsanity.com/paper/2605.18579/) (2026)

## Abstract (arXiv)

Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily rely on high-quality user generated content (including user profiles) or suffer from data insufficiency problem if only focusing on network topology, which brings researchers into an insoluble dilemma of model selection. In this paper, to address this problem, we propose a novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner. The proposed method overcomes data insufficiency problem of existing work and does not necessarily rely on user demographic information. Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks. Extensive experiments have been conducted on two large-scale real-life social networks. The experimental results demonstrate that the proposed method outperforms the state-of-the-art models with a big margin.