# Urban Region Profiling via A Multi-Graph Representation Learning Framework

arXiv: https://arxiv.org/abs/2202.02074 · HTML: https://mlsanity.com/paper/2202.02074/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [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)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [An Attention-based Collaboration Framework for Multi-View Network Representation Learning](https://mlsanity.com/paper/1709.06636/) (2017)
- [Deep convolutional acoustic word embeddings using word-pair side information](https://mlsanity.com/paper/1510.01032/) (2015)
- [Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement](https://mlsanity.com/paper/1811.02616/) (2018)
- [The Power of Side-information in Subgraph Detection](https://mlsanity.com/paper/1611.04847/) (2016)

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

- [Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction](https://mlsanity.com/paper/2411.00028/) (2024)
- [Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning](https://mlsanity.com/paper/2402.06135/) (2024)
- [Attentive Graph Enhanced Region Representation Learning](https://mlsanity.com/paper/2307.03212/) (2023)

## Abstract (arXiv)

Urban region profiling can benefit urban analytics. Although existing studies have made great efforts to learn urban region representation from multi-source urban data, there are still three limitations: (1) Most related methods focused merely on global-level inter-region relations while overlooking local-level geographical contextual signals and intra-region information; (2) Most previous works failed to develop an effective yet integrated fusion module which can deeply fuse multi-graph correlations; (3) State-of-the-art methods do not perform well in regions with high variance socioeconomic attributes. To address these challenges, we propose a multi-graph representative learning framework, called Region2Vec, for urban region profiling. Specifically, except that human mobility is encoded for inter-region relations, geographic neighborhood is introduced for capturing geographical contextual information while POI side information is adopted for representing intra-region information by knowledge graph. Then, graphs are used to capture accessibility, vicinity, and functionality correlations among regions. To consider the discriminative properties of multiple graphs, an encoder-decoder multi-graph fusion module is further proposed to jointly learn comprehensive representations. Experiments on real-world datasets show that Region2Vec can be employed in three applications and outperforms all state-of-the-art baselines. Particularly, Region2Vec has better performance than previous studies in regions with high variance socioeconomic attributes.