# Research on Domain Information Mining and Theme Evolution of Scientific Papers

arXiv: https://arxiv.org/abs/2204.08476 · HTML: https://mlsanity.com/paper/2204.08476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 91 references that are in the map.
- First posted on arXiv 18 Apr 2022.

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

- [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)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks](https://mlsanity.com/paper/1508.00200/) (2015)
- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification](https://mlsanity.com/paper/1609.02521/) (2016)
- [Joint Embedding of Words and Labels for Text Classification](https://mlsanity.com/paper/1805.04174/) (2018)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [Predicting Path Failure In Time-Evolving Graphs](https://mlsanity.com/paper/1905.03994/) (2019)
- [GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction](https://mlsanity.com/paper/1812.04206/) (2018)
- [Self-Supervised Graph Co-Training for Session-based Recommendation](https://mlsanity.com/paper/2108.10560/) (2021)
- [R-Transformer: Recurrent Neural Network Enhanced Transformer](https://mlsanity.com/paper/1907.05572/) (2019)
- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [Neural Link Prediction with Walk Pooling](https://mlsanity.com/paper/2110.04375/) (2021)
- [Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval](https://mlsanity.com/paper/2203.08615/) (2022)
- [Hyperbolic Interaction Model For Hierarchical Multi-Label Classification](https://mlsanity.com/paper/1905.10802/) (2019)
- [Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks](https://mlsanity.com/paper/1909.06627/) (2019)
- [Deep Extreme Multi-label Learning](https://mlsanity.com/paper/1704.03718/) (2017)
- [Learning Deep Latent Spaces for Multi-Label Classification](https://mlsanity.com/paper/1707.00418/) (2017)
- [Collaboration based Multi-Label Learning](https://mlsanity.com/paper/1902.03047/) (2019)

## Abstract (arXiv)

In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Cross-disciplinary research results have gradually become an emerging frontier research direction. There is a certain dependence between a large number of research results. It is difficult to effectively analyze today's scientific research results when looking at a single research field in isolation. How to effectively use the huge number of scientific papers to help researchers becomes a challenge. This paper introduces the research status at home and abroad in terms of domain information mining and topic evolution law of scientific and technological papers from three aspects: the semantic feature representation learning of scientific and technological papers, the field information mining of scientific and technological papers, and the mining and prediction of research topic evolution rules of scientific and technological papers.