# Graph Force Learning

arXiv: https://arxiv.org/abs/2103.04344 · HTML: https://mlsanity.com/paper/2103.04344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [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)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks](https://mlsanity.com/paper/1907.03395/) (2019)
- [A Survey on Network Embedding](https://mlsanity.com/paper/1711.08752/) (2017)
- [End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion](https://mlsanity.com/paper/1811.04441/) (2018)
- [Graphite: Iterative Generative Modeling of Graphs](https://mlsanity.com/paper/1803.10459/) (2018)
- [Graph Convolutional Networks with EigenPooling](https://mlsanity.com/paper/1904.13107/) (2019)
- [Session-based Social Recommendation via Dynamic Graph Attention Networks](https://mlsanity.com/paper/1902.09362/) (2019)
- [Deep Spectral Clustering using Dual Autoencoder Network](https://mlsanity.com/paper/1904.13113/) (2019)
- [A Neural Influence Diffusion Model for Social Recommendation](https://mlsanity.com/paper/1904.10322/) (2019)
- [Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining](https://mlsanity.com/paper/2008.07097/) (2020)
- [Weakly Labelled AudioSet Tagging with Attention Neural Networks](https://mlsanity.com/paper/1903.00765/) (2019)
- [Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach](https://mlsanity.com/paper/1808.00703/) (2018)
- [Effective Decoding in Graph Auto-Encoder using Triadic Closure](https://mlsanity.com/paper/1911.11322/) (2019)
- [Multivariate Relations Aggregation Learning in Social Networks](https://mlsanity.com/paper/2008.03654/) (2020)
- [A Distributed Multilevel Force-directed Algorithm](https://mlsanity.com/paper/1608.08522/) (2016)

## Built on by (most cited first, 3 of 3)

- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Physics-Informed Graph Learning](https://mlsanity.com/paper/2202.10679/) (2022)
- [Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method](https://mlsanity.com/paper/2306.11307/) (2023)

## Abstract (arXiv)

Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning.