# Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay

arXiv: https://arxiv.org/abs/2605.00374 · HTML: https://mlsanity.com/paper/2605.00374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 1 May 2026.

## Builds on (most cited first, 24 of 24)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Graph Neural Networks with Heterophily](https://mlsanity.com/paper/2009.13566/) (2020)
- [Edge-labeling Graph Neural Network for Few-shot Learning](https://mlsanity.com/paper/1905.01436/) (2019)
- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [Community Interaction and Conflict on the Web](https://mlsanity.com/paper/1803.03697/) (2018)
- [Learning from Counterfactual Links for Link Prediction](https://mlsanity.com/paper/2106.02172/) (2021)
- [NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2011.09144/) (2020)
- [Causal Inference in Recommender Systems: A Survey and Future Directions](https://mlsanity.com/paper/2208.12397/) (2022)
- [Knowledge Graph Large Language Model (KG-LLM) for Link Prediction](https://mlsanity.com/paper/2403.07311/) (2024)
- [Global Counterfactual Explainer for Graph Neural Networks](https://mlsanity.com/paper/2210.11695/) (2022)
- [A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges](https://mlsanity.com/paper/2210.12089/) (2022)
- [Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2202.01336/) (2022)
- [Counterfactual Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.01391/) (2023)
- [A Topological Perspective on Demystifying GNN-Based Link Prediction Performance](https://mlsanity.com/paper/2310.04612/) (2023)
- [Adversarially Balanced Representation for Continuous Treatment Effect Estimation](https://mlsanity.com/paper/2312.10570/) (2023)
- [Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement](https://mlsanity.com/paper/2407.20499/) (2024)
- [ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations](https://mlsanity.com/paper/1911.05467/) (2019)
- [Learning Fair Models without Sensitive Attributes: A Generative Approach](https://mlsanity.com/paper/2203.16413/) (2022)
- [AttrE2vec: Unsupervised Attributed Edge Representation Learning](https://mlsanity.com/paper/2012.14727/) (2020)
- [Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth](https://mlsanity.com/paper/2408.12803/) (2024)
- [Edge Classification on Graphs: New Directions in Topological Imbalance](https://mlsanity.com/paper/2406.11685/) (2024)
- [Signed Node Relevance Measurements](https://mlsanity.com/paper/1710.07236/) (2017)

## Abstract (arXiv)

Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overlook the potential causal influences of node features on edge features, leading to a loss of relevant prior information. In this work, we present an empirical exploration using the Causal Edge Classification Framework (CECF). Unlike conventional causal inference methods, CECF is the first framework to apply causal inference principles to the edge classification task and to explore modeling edge features as a high-dimensional treatment within a causal framework. Based on the node embedding of Graph Neural Network (GNN), CECF seeks to learn a balanced representation of high-dimensional edge features by mitigating the potential influence of node features. Then, a cross-attention network captures the complex dependencies between node and edge features for final edge classification. Extensive experiments demonstrate that CECF not only achieves superior performance but also serves as a flexible, plug-and-play enhancement for existing methods. We also provide empirical analyses, offering insights into when and how this high-dimensional causal modeling framework works for the edge classification.