Variational Community Partition with Novel Network Structure Centrality Prior

11/12/2018
by   Yiguang Bai, et al.
0

In this paper, we proposed a novel two-stage optimization method for network community partition, which is based on inherent network structure information. The introduced optimization approach utilizes the new network centrality measure of both links and vertices to construct the key affinity description of the given network, where the direct similarities between graph nodes or nodal features are not available to obtain the classical affinity matrix. Indeed, such calculated network centrality information presents the essential structure of network, hence, the proper measure for detecting network communities, which also introduces a `confidence' criterion for referencing new labeled benchmark nodes. For the resulted challenging combinatorial optimization problem of graph clustering, the proposed optimization method iteratively employs an efficient convex optimization algorithm which is developed based under a new variational perspective of primal and dual. Experiments over both artificial and real-world network datasets demonstrate that the proposed optimization strategy of community detection significantly improves result accuracy and outperforms the state-of-the-art algorithms in terms of accuracy and reliability.

READ FULL TEXT

page 2

page 3

page 5

page 7

page 10

page 11

page 14

page 15

research
04/12/2018

Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks

Affinity propagation is one of the most effective algorithms for data cl...
research
07/26/2018

Simplex Representation for Subspace Clustering

Spectral clustering based methods have achieved leading performance on s...
research
01/28/2019

Detecting Multiple Communities Using Quantum Annealing on the D-Wave System

A very important problem in combinatorial optimization is partitioning a...
research
03/04/2022

Quantum Approximate Optimization Algorithm for Bayesian network structure learning

Bayesian network structure learning is an NP-hard problem that has been ...
research
03/21/2022

FaceMap: Towards Unsupervised Face Clustering via Map Equation

Face clustering is an essential task in computer vision due to the explo...
research
11/28/2022

Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes

Network structure evolves with time in the real world, and the discovery...
research
02/05/2021

ROBustness In Network (robin): an R package for Comparison and Validation of communities

In network analysis, many community detection algorithms have been devel...

Please sign up or login with your details

Forgot password? Click here to reset