# CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks

arXiv: https://arxiv.org/abs/2210.08811 · HTML: https://mlsanity.com/paper/2210.08811/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Unsupervised Attributed Multiplex Network Embedding](https://mlsanity.com/paper/1911.06750/) (2019)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Counterfactual Graphs for Explainable Classification of Brain Networks](https://mlsanity.com/paper/2106.08640/) (2021)
- [Explainable Classification of Brain Networks via Contrast Subgraphs](https://mlsanity.com/paper/2006.05176/) (2020)
- [A Survey of Community Search Over Big Graphs](https://mlsanity.com/paper/1904.12539/) (2019)
- [Approximate Closest Community Search in Networks](https://mlsanity.com/paper/1505.05956/) (2015)
- [Attribute Truss Community Search](https://mlsanity.com/paper/1609.00090/) (2016)
- [Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed](https://mlsanity.com/paper/2104.03583/) (2021)
- [FirmCore Decomposition of Multilayer Networks](https://mlsanity.com/paper/2208.11200/) (2022)
- [Deep Partial Multiplex Network Embedding](https://mlsanity.com/paper/2203.02656/) (2022)
- [Core Decomposition in Multilayer Networks: Theory, Algorithms, and Applications](https://mlsanity.com/paper/1812.08712/) (2018)
- [Multi-attributed Community Search in Road-social Networks](https://mlsanity.com/paper/2101.09668/) (2021)
- [Butterfly-Core Community Search over Labeled Graphs](https://mlsanity.com/paper/2105.08628/) (2021)
- [FirmTruss Community Search in Multilayer Networks](https://mlsanity.com/paper/2205.00742/) (2022)
- [Geo-Social Group Queries with Minimum Acquaintance Constraint](https://mlsanity.com/paper/1406.7367/) (2014)

## Built on by (most cited first, 5 of 5)

- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [CS-TGN: Community Search via Temporal Graph Neural Networks](https://mlsanity.com/paper/2303.08964/) (2023)
- [Mapping Text to Multiplex Graph: Prompt Compression as L'evy Walk-Guided Graph Pruning](https://mlsanity.com/paper/2607.01241/) (2026)
- [MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding](https://mlsanity.com/paper/2403.19246/) (2024)
- [An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals](https://mlsanity.com/paper/2411.01134/) (2024)

## Abstract (arXiv)

Community Search (CS) is one of the fundamental tasks in network science and has attracted much attention due to its ability to discover personalized communities with a wide range of applications. Given any query nodes, CS seeks to find a densely connected subgraph containing query nodes. Most existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in many applications such as biological, social, and transportation networks, interactions between objects span multiple aspects, yielding networks with multiple views, called multiplex networks. Existing CS approaches in multiplex networks adopt pre-defined subgraph patterns to model the communities, which cannot find communities that do not have such pre-defined patterns in real-world networks. In this paper, we propose a query-driven graph convolutional network in multiplex networks, CS-MLGCN, that can capture flexible community structures by learning from the ground-truth communities in a data-driven fashion. CS-MLGCN first combines the local query-dependent structure and global graph embedding in each type of proximity and then uses an attention mechanism to incorporate information on different types of relations. Experiments on real-world graphs with ground-truth communities validate the quality of the solutions we obtain and the efficiency of our model.