# An Effective Index for Truss-based Community Search on Large Directed Graphs

arXiv: https://arxiv.org/abs/2401.10641 · HTML: https://mlsanity.com/paper/2401.10641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 19 Jan 2024.

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

- [Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations](https://mlsanity.com/paper/2312.06337/) (2023)
- [A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning](https://mlsanity.com/paper/2312.05735/) (2023)
- [Graph Information Bottleneck for Remote Sensing Segmentation](https://mlsanity.com/paper/2312.02545/) (2023)
- [DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition](https://mlsanity.com/paper/2312.10579/) (2023)
- [A Survey of Community Search Over Big Graphs](https://mlsanity.com/paper/1904.12539/) (2019)
- [CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation](https://mlsanity.com/paper/2312.01758/) (2023)
- [Community detection in networks: Structural communities versus ground truth](https://mlsanity.com/paper/1406.0146/) (2014)
- [Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition](https://mlsanity.com/paper/2312.16778/) (2023)
- [Attribute Truss Community Search](https://mlsanity.com/paper/1609.00090/) (2016)
- [Geo-Social Group Queries with Minimum Acquaintance Constraint](https://mlsanity.com/paper/1406.7367/) (2014)

## Abstract (arXiv)

Community search is a derivative of community detection that enables online and personalized discovery of communities and has found extensive applications in massive real-world networks. Recently, there needs to be more focus on the community search issue within directed graphs, even though substantial research has been carried out on undirected graphs. The recently proposed D-truss model has achieved good results in the quality of retrieved communities. However, existing D-truss-based work cannot perform efficient community searches on large graphs because it consumes too many computing resources to retrieve the maximal D-truss. To overcome this issue, we introduce an innovative merge relation known as D-truss-connected to capture the inherent density and cohesiveness of edges within D-truss. This relation allows us to partition all the edges in the original graph into a series of D-truss-connected classes. Then, we construct a concise and compact index, ConDTruss, based on D-truss-connected. Using ConDTruss, the efficiency of maximum D-truss retrieval will be greatly improved, making it a theoretically optimal approach. Experimental evaluations conducted on large directed graph certificate the effectiveness of our proposed method.