# Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection

arXiv: https://arxiv.org/abs/2212.01515 · HTML: https://mlsanity.com/paper/2212.01515/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Learning Sparse Neural Networks through L0 Regularization](https://mlsanity.com/paper/1712.01312/) (2017)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Towards Deeper Graph Neural Networks](https://mlsanity.com/paper/2007.09296/) (2020)
- [Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings](https://mlsanity.com/paper/2006.13009/) (2020)
- [Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking](https://mlsanity.com/paper/2010.00577/) (2020)
- [Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.12362/) (2020)
- [GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://mlsanity.com/paper/1908.01843/) (2019)
- [Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://mlsanity.com/paper/1908.04942/) (2019)
- [GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension](https://mlsanity.com/paper/1908.00059/) (2019)
- [PANDORA Talks: Personality and Demographics on Reddit](https://mlsanity.com/paper/2004.04460/) (2020)
- [Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings](https://mlsanity.com/paper/1911.09304/) (2019)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models](https://mlsanity.com/paper/1907.06333/) (2019)
- [PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship](https://mlsanity.com/paper/1811.08657/) (2018)
- [Psycholinguistic Tripartite Graph Network for Personality Detection](https://mlsanity.com/paper/2106.04963/) (2021)

## Built on by (most cited first, 14 of 14)

- [LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model](https://mlsanity.com/paper/2403.07581/) (2024)
- [Identifying Multiple Personalities in Large Language Models with External Evaluation](https://mlsanity.com/paper/2402.14805/) (2024)
- [EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection](https://mlsanity.com/paper/2406.16079/) (2024)
- [Less but Better: Parameter-Efficient Fine-Tuning of Large Language Models for Personality Detection](https://mlsanity.com/paper/2504.05411/) (2025)
- [Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives](https://mlsanity.com/paper/2404.15067/) (2024)
- [LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection](https://mlsanity.com/paper/2504.02146/) (2025)
- [Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection](https://mlsanity.com/paper/2408.08551/) (2024)
- [EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling](https://mlsanity.com/paper/2509.02450/) (2025)
- [Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models](https://mlsanity.com/paper/2512.06991/) (2025)
- [HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection](https://mlsanity.com/paper/2510.09893/) (2025)
- [Enhancing Personality Recognition in Dialogue by Data Augmentation and Heterogeneous Conversational Graph Networks](https://mlsanity.com/paper/2401.05871/) (2024)
- [Cognitive Alignment in Personality Reasoning: Leveraging Prototype Theory for MBTI Inference](https://mlsanity.com/paper/2511.00115/) (2025)
- [Ask, Answer, and Detect: Role-Playing LLMs for Personality Detection with Question-Conditioned Mixture-of-Experts](https://mlsanity.com/paper/2512.08814/) (2025)
- [Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition](https://mlsanity.com/paper/2607.08374/) (2026)

## Abstract (arXiv)

Predicting personality traits based on online posts has emerged as an important task in many fields such as social network analysis. One of the challenges of this task is assembling information from various posts into an overall profile for each user. While many previous solutions simply concatenate the posts into a long document and then encode the document by sequential or hierarchical models, they introduce unwarranted orders for the posts, which may mislead the models. In this paper, we propose a dynamic deep graph convolutional network (D-DGCN) to overcome the above limitation. Specifically, we design a learn-to-connect approach that adopts a dynamic multi-hop structure instead of a deterministic structure, and combine it with a DGCN module to automatically learn the connections between posts. The modules of post encoder, learn-to-connect, and DGCN are jointly trained in an end-to-end manner. Experimental results on the Kaggle and Pandora datasets show the superior performance of D-DGCN to state-of-the-art baselines. Our code is available at https://github.com/djz233/D-DGCN.