# CS-TGN: Community Search via Temporal Graph Neural Networks

arXiv: https://arxiv.org/abs/2303.08964 · HTML: https://mlsanity.com/paper/2303.08964/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 57 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 22 of 22)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [Predicting Path Failure In Time-Evolving Graphs](https://mlsanity.com/paper/1905.03994/) (2019)
- [Counterfactual Graphs for Explainable Classification of Brain Networks](https://mlsanity.com/paper/2106.08640/) (2021)
- [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)
- [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)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks](https://mlsanity.com/paper/2210.08811/) (2022)
- [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)
- [On the Formation of Circles in Co-authorship Networks](https://mlsanity.com/paper/1505.04560/) (2015)
- [Adaptive Community Search in Dynamic Networks](https://mlsanity.com/paper/2011.05353/) (2020)
- [Reliable Community Search in Dynamic Networks](https://mlsanity.com/paper/2202.01525/) (2022)

## Built on by (most cited first, 4 of 4)

- [State Space Models on Temporal Graphs: A First-Principles Study](https://mlsanity.com/paper/2406.00943/) (2024)
- [CAT-Walk: Inductive Hypergraph Learning via Set Walks](https://mlsanity.com/paper/2306.11147/) (2023)
- [LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning](https://mlsanity.com/paper/2311.16605/) (2023)
- [Changepoint Detection in Highly-Attributed Dynamic Graphs](https://mlsanity.com/paper/2407.06998/) (2024)

## Abstract (arXiv)

Searching for local communities is an important research challenge that allows for personalized community discovery and supports advanced data analysis in various complex networks, such as the World Wide Web, social networks, and brain networks. The evolution of these networks over time has motivated several recent studies to identify local communities in temporal networks. Given any query nodes, Community Search aims to find a densely connected subgraph containing query nodes. However, existing community search approaches in temporal networks have two main limitations: (1) they adopt pre-defined subgraph patterns to model communities, which cannot find communities that do not conform to these patterns in real-world networks, and (2) they only use the aggregation of disjoint structural information to measure quality, missing the dynamic of connections and temporal properties. In this paper, we propose a query-driven Temporal Graph Convolutional Network (CS-TGN) that can capture flexible community structures by learning from the ground-truth communities in a data-driven manner. CS-TGN first combines the local query-dependent structure and the global graph embedding in each snapshot of the network and then uses a GRU cell with contextual attention to learn the dynamics of interactions and update node embeddings over time. We demonstrate how this model can be used for interactive community search in an online setting, allowing users to evaluate the found communities and provide feedback. Experiments on real-world temporal graphs with ground-truth communities validate the superior quality of the solutions obtained and the efficiency of our model in both temporal and interactive static settings.