# Representation Learning of Complex Assemblies, An Effort to Improve Corporate Scope 3 Emissions Calculation

arXiv: https://arxiv.org/abs/2409.03769 · HTML: https://mlsanity.com/paper/2409.03769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [Adaptive Universal Generalized PageRank Graph Neural Network](https://mlsanity.com/paper/2006.07988/) (2020)
- [Multi-scale Attributed Node Embedding](https://mlsanity.com/paper/1909.13021/) (2019)
- [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](https://mlsanity.com/paper/1905.00067/) (2019)
- [Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods](https://mlsanity.com/paper/2110.14446/) (2021)
- [Revisiting Graph Neural Networks: All We Have is Low-Pass Filters](https://mlsanity.com/paper/1905.09550/) (2019)
- [Graph Neural Networks with Heterophily](https://mlsanity.com/paper/2009.13566/) (2020)
- [Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks](https://mlsanity.com/paper/2102.06462/) (2021)
- [Node Similarity Preserving Graph Convolutional Networks](https://mlsanity.com/paper/2011.09643/) (2020)
- [How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision](https://mlsanity.com/paper/2204.04879/) (2022)
- [Non-Local Graph Neural Networks](https://mlsanity.com/paper/2005.14612/) (2020)
- [Incorporating Literals into Knowledge Graph Embeddings](https://mlsanity.com/paper/1802.00934/) (2018)
- [Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks](https://mlsanity.com/paper/2004.04834/) (2020)
- [Link Prediction on Heterophilic Graphs via Disentangled Representation Learning](https://mlsanity.com/paper/2208.01820/) (2022)

## Abstract (arXiv)

Climate change is a pressing global concern for governments, corporations, and citizens alike. This concern underscores the necessity for these entities to accurately assess the climate impact of manufacturing goods and providing services. Tools like process life cycle analysis (pLCA) are used to evaluate the climate impact of production, use, and disposal, from raw material mining through end-of-life. pLCA further enables practitioners to look deeply into material choices or manufacturing processes for individual parts, sub-assemblies, assemblies, and the final product. Reliable and detailed data on the life cycle stages and processes of the product or service under study are not always available or accessible, resulting in inaccurate assessment of climate impact. To overcome the data limitation and enhance the effectiveness of pLCA to generate an improved environmental impact profile, we are adopting an innovative strategy to identify alternative parts, products, and components that share similarities in terms of their form, function, and performance to serve as qualified substitutes. Focusing on enterprise electronics hardware, we propose a semi-supervised learning-based framework to identify substitute parts that leverages product bill of material (BOM) data and a small amount of component-level qualified substitute data (positive samples) to generate machine knowledge graph (MKG) and learn effective embeddings of the components that constitute electronic hardware. Our methodology is grounded in attributed graph embeddings and introduces a strategy to generate biased negative samples to significantly enhance the training process. We demonstrate improved performance and generalization over existing published models.