# Generalized Reciprocal Perspective

arXiv: https://arxiv.org/abs/2210.11616 · HTML: https://mlsanity.com/paper/2210.11616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [SimplE Embedding for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1802.04868/) (2018)
- [Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration](https://mlsanity.com/paper/2109.14285/) (2021)
- [HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems](https://mlsanity.com/paper/1809.01703/) (2018)
- [Transductive Unbiased Embedding for Zero-Shot Learning](https://mlsanity.com/paper/1803.11320/) (2018)
- [Stacking Models for Nearly Optimal Link Prediction in Complex Networks](https://mlsanity.com/paper/1909.07578/) (2019)
- [Probability Calibration for Knowledge Graph Embedding Models](https://mlsanity.com/paper/1912.10000/) (2019)
- [Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning](https://mlsanity.com/paper/1611.02945/) (2016)

## Abstract (arXiv)

Across many domains, real-world problems can be represented as a network. Nodes represent domain-specific elements and edges capture the relationship between elements. Leveraging high-performance computing and optimized link prediction algorithms, it is increasingly possible to evaluate every possible combination of nodal pairs enabling the generation of a comprehensive prediction matrix (CPM) that places an individual link prediction score in the context of all possible links involving either node (providing data-driven context). Historically, this contextual information has been ignored given exponentially growing problem sizes resulting in computational intractability; however, we demonstrate that expending high-performance compute resources to generate CPMs is a worthwhile investment given the improvement in predictive performance. In this work, we generalize for all pairwise link-prediction tasks our novel semi-supervised machine learning method, denoted Reciprocal Perspective (RP). We demonstrate that RP significantly improves link prediction accuracy by leveraging the wealth of information in a CPM. Context-based features are extracted from the CPM for use in a stacked classifier and we demonstrate that the application of RP in a cascade almost always results in significantly (p < 0.05) improved predictions. These results on RS-type problems suggest that RP is applicable to a broad range of link prediction problems.