# Fast Algorithm for K-Truss Discovery on Public-Private Graphs

## TL;DR

This paper introduces an efficient incremental algorithm for discovering k-truss dense subgraphs in public-private graphs, addressing privacy concerns and improving computational performance.

## Contribution

It proposes a novel node insertion update algorithm and a hybrid strategy for incremental k-truss computation in public-private graphs.

## Key findings

- Outperforms existing methods on real-world datasets
- Efficiently updates k-truss with node insertions
- Validates effectiveness through extensive experiments

## Abstract

In public-private graphs, users share one public graph and have their own private graphs. A private graph consists of personal private contacts that only can be visible to its owner, e.g., hidden friend lists on Facebook and secret following on Sina Weibo. However, existing public-private analytic algorithms have not yet investigated the dense subgraph discovery of k-truss, where each edge is contained in at least k-2 triangles. This paper aims at finding k-truss efficiently in public-private graphs. The core of our solution is a novel algorithm to update k-truss with node insertions. We develop a classification-based hybrid strategy of node insertions and edge insertions to incrementally compute k-truss in public-private graphs. Extensive experiments validate the superiority of our proposed algorithms against state-of-the-art methods on real-world datasets.