# Inferring Degrees from Incomplete Networks and Nonlinear Dynamics

arXiv: https://arxiv.org/abs/2004.10546 · HTML: https://mlsanity.com/paper/2004.10546/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 30 of 33)

- [Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding](https://mlsanity.com/paper/2203.01515/) (2022)
- [Revisiting Transformer-based Models for Long Document Classification](https://mlsanity.com/paper/2204.06683/) (2022)
- [PLM-ICD: Automatic ICD Coding with Pretrained Language Models](https://mlsanity.com/paper/2207.05289/) (2022)
- [Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study](https://mlsanity.com/paper/2304.10909/) (2023)
- [Towards BERT-based Automatic ICD Coding: Limitations and Opportunities](https://mlsanity.com/paper/2104.06709/) (2021)
- [Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines](https://mlsanity.com/paper/2107.10650/) (2021)
- [Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification](https://mlsanity.com/paper/2304.13998/) (2023)
- [MDACE: MIMIC Documents Annotated with Code Evidence](https://mlsanity.com/paper/2307.03859/) (2023)
- [A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries](https://mlsanity.com/paper/2107.10652/) (2021)
- [PyHealth: A Python Library for Health Predictive Models](https://mlsanity.com/paper/2101.04209/) (2021)
- [Multitask Balanced and Recalibrated Network for Medical Code Prediction](https://mlsanity.com/paper/2109.02418/) (2021)
- [A Two-Stage Decoder for Efficient ICD Coding](https://mlsanity.com/paper/2306.00005/) (2023)
- [Modelling Temporal Document Sequences for Clinical ICD Coding](https://mlsanity.com/paper/2302.12666/) (2023)
- [MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention](https://mlsanity.com/paper/2312.05741/) (2023)
- [HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding](https://mlsanity.com/paper/2208.02301/) (2022)
- [Modeling Diagnostic Label Correlation for Automatic ICD Coding](https://mlsanity.com/paper/2106.12800/) (2021)
- [Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models](https://mlsanity.com/paper/2212.05857/) (2022)
- [Hierarchical BERT for Medical Document Understanding](https://mlsanity.com/paper/2204.09600/) (2022)
- [Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics](https://mlsanity.com/paper/2201.04194/) (2022)
- [HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding](https://mlsanity.com/paper/2212.04891/) (2022)
- [TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics](https://mlsanity.com/paper/2408.09825/) (2024)
- [A Meta-embedding-based Ensemble Approach for ICD Coding Prediction](https://mlsanity.com/paper/2102.13622/) (2021)
- [A Pseudo Label-wise Attention Network for Automatic ICD Coding](https://mlsanity.com/paper/2106.06822/) (2021)
- [Parameter Selection: Why We Should Pay More Attention to It](https://mlsanity.com/paper/2107.05393/) (2021)
- [An Automatic ICD Coding Network Using Partition-Based Label Attention](https://mlsanity.com/paper/2211.08429/) (2022)
- [JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling with Slot-to-Intent Attention](https://mlsanity.com/paper/2312.08737/) (2023)
- [Content Reduction, Surprisal and Information Density Estimation for Long Documents](https://mlsanity.com/paper/2309.06009/) (2023)
- [MHLAT: Multi-hop Label-wise Attention Model for Automatic ICD Coding](https://mlsanity.com/paper/2309.08868/) (2023)
- [Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification](https://mlsanity.com/paper/2311.16650/) (2023)
- [Automated Clinical Coding for Outpatient Departments](https://mlsanity.com/paper/2312.13533/) (2023)

## Abstract (arXiv)

Inferring topological characteristics of complex networks from observed data is critical to understand the dynamical behavior of networked systems, ranging from the Internet and the World Wide Web to biological networks and social networks. Prior studies usually focus on the structure-based estimation to infer network sizes, degree distributions, average degrees, and more. Little effort attempted to estimate the specific degree of each vertex from a sampled induced graph, which prevents us from measuring the lethality of nodes in protein networks and influencers in social networks. The current approaches dramatically fail for a tiny sampled induced graph and require a specific sampling method and a large sample size. These approaches neglect information of the vertex state, representing the dynamical behavior of the networked system, such as the biomass of species or expression of a gene, which is useful for degree estimation. We fill this gap by developing a framework to infer individual vertex degrees using both information of the sampled topology and vertex state. We combine the mean-field theory with combinatorial optimization to learn vertex degrees. Experimental results on real networks with a variety of dynamics demonstrate that our framework can produce reliable degree estimates and dramatically improve existing link prediction methods by replacing the sampled degrees with our estimated degrees.