DeepAI AI Chat
Log In Sign Up

Label propagation on binomial random graphs

by   Marcos Kiwi, et al.

We study a variant of the widely popular, fast and often used “family” of community detection procedures referred to as label propagation algorithms. Initially, given a network, each vertex starts with a random label in the interval [0,1]. Then, in each round of the algorithm, every vertex switches its label to the majority label in its neighborhood (including its own label). At the first round, ties are broken towards smaller labels, while at each of the next rounds, ties are broken uniformly at random. We investigate the performance of this algorithm on the binomial random graph 𝒢(n,p). We show that for np ≥ n^5/8+ε, the algorithm terminates with a single label a.a.s. (which was previously known only for np≥ n^3/4+ε). Moreover, we show that if np≫ n^2/3, a.a.s. this label is the smallest one, whereas if n^5/8+ε≤ np≪ n^2/3, the surviving label is a.a.s. not the smallest one.


page 1

page 2

page 3

page 4


Semi-supervised evidential label propagation algorithm for graph data

In the task of community detection, there often exists some useful prior...

Efficient inference in stochastic block models with vertex labels

We study the stochastic block model with two communities where vertices ...

Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection

Community detection is an essential task in network analysis as it helps...

Evidential Label Propagation Algorithm for Graphs

Community detection has attracted considerable attention crossing many a...

Burning Number for the Points in the Plane

The burning process on a graph G starts with a single burnt vertex, and ...

The Circuit Complexity of Inference

Belief propagation is one of the foundations of probabilistic and causal...

Ensemble Teaching for Hybrid Label Propagation

Label propagation aims to iteratively diffuse the label information from...