Name
Abstract | ||
|---|---|---|
Community detection in inhomogeneous structured network is an attractive research problem that searches for methods to discover groups in which individuals are more densely interconnected with each other with higher probability of internal information propagation. While most of the previous approaches attempt to divide networks into communities according to the algorithm results of network or edge measurement, Label Propagation Algorithm (LPA) adopts semi-supervised machine learning and implements community detection in an intelligent way with the automatic convergent process of network entity label iteration. In this work, we study the early community detection approaches, explore the low efficacy and stagnant converging rate of LPA in its response to network with overlapped communities, and propose a new approach for community detection using epidemic spreading virus to discover groups with super positioned members. Extensive experiments in synthetic signed network and real-life large networks derived from Internet social media are conducted to explore the optimal mechanism of the most suitable community-detecting virus infection. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ASONAM.2014.6921701 | ASONAM |
Keywords | Field | DocType |
inhomogeneous structured network,network measurement,real-life large networks,label propagation algorithm,edge measurement,learning (artificial intelligence),epidemic spreading model based overlapping community detection,automatic convergent process,epidemic spreading,microorganisms,synthetic signed network,lpa,network entity label iteration,semisupervised machine learning,epidemics,internet,internal information propagation probability,medical computing,internet social media,social networking (online),community-detecting virus infection,community detection,group discovery,media,kernel,mathematical model | Kernel (linear algebra),Data mining,Large networks,Social media,Optimal mechanism,Label propagation,Computer science,Artificial intelligence,Information propagation,Machine learning,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-4799-5876-4 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Wen | 1 | 31 | 4.71 |
| Yuanhao Chen | 2 | 325 | 30.63 |
| Xiao Long Deng | 3 | 14 | 4.72 |