# Improving Peer Assessment with Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2111.04466 · HTML: https://mlsanity.com/paper/2111.04466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 55 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [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)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [Unifying Graph Convolutional Neural Networks and Label Propagation](https://mlsanity.com/paper/2002.06755/) (2020)
- [Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks](https://mlsanity.com/paper/1906.02174/) (2019)
- [Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments](https://mlsanity.com/paper/2006.16437/) (2020)
- [AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models](https://mlsanity.com/paper/1908.05081/) (2019)
- [Crowdsourced Judgement Elicitation with Endogenous Proficiency](https://mlsanity.com/paper/1303.0799/) (2013)
- [Attack Graph Convolutional Networks by Adding Fake Nodes](https://mlsanity.com/paper/1810.10751/) (2018)
- [Informed Truthfulness in Multi-Task Peer Prediction](https://mlsanity.com/paper/1603.03151/) (2016)
- [On Strategyproof Conference Peer Review](https://mlsanity.com/paper/1806.06266/) (2018)
- [Learning Graph Normalization for Graph Neural Networks](https://mlsanity.com/paper/2009.11746/) (2020)
- [Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment](https://mlsanity.com/paper/2010.04041/) (2020)
- [Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment](https://mlsanity.com/paper/1604.03632/) (2016)
- [Tuned Models of Peer Assessment in MOOCs](https://mlsanity.com/paper/1307.2579/) (2013)
- [The PeerRank Method for Peer Assessment](https://mlsanity.com/paper/1405.7192/) (2014)
- [Report-Sensitive Spot-Checking in Peer-Grading Systems](https://mlsanity.com/paper/1906.05884/) (2019)
- [Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines](https://mlsanity.com/paper/1506.00852/) (2015)
- [Incentives for Truthful Peer Grading](https://mlsanity.com/paper/1604.03178/) (2016)
- [Putting Peer Prediction Under the Micro(economic)scope and Making Truth-telling Focal](https://mlsanity.com/paper/1603.07319/) (2016)
- [CrowdGrader: Crowdsourcing the Evaluation of Homework Assignments](https://mlsanity.com/paper/1308.5273/) (2013)

## Abstract (arXiv)

Peer assessment systems are emerging in many social and multi-agent settings, such as peer grading in large (online) classes, peer review in conferences, peer art evaluation, etc. However, peer assessments might not be as accurate as expert evaluations, thus rendering these systems unreliable. The reliability of peer assessment systems is influenced by various factors such as assessment ability of peers, their strategic assessment behaviors, and the peer assessment setup (e.g., peer evaluating group work or individual work of others). In this work, we first model peer assessment as multi-relational weighted networks that can express a variety of peer assessment setups, plus capture conflicts of interest and strategic behaviors. Leveraging our peer assessment network model, we introduce a graph convolutional network which can learn assessment patterns and user behaviors to more accurately predict expert evaluations. Our extensive experiments on real and synthetic datasets demonstrate the efficacy of our proposed approach, which outperforms existing peer assessment methods.