1 Label Propagation for Clustering 1 Lovro Šubelj 1.1 Label Propagation Method 1 1.1.1 Resolution of Label Ties 3 1.1.2 Order of Label Propagation 3 1.1.3 Label Equilibrium Criterium 4 1.1.4 Algorithm and Complexity 5 1.2 Label Propagation as Optimization 6 1.3 Advances of Label Propagation 7 1.3.1 Label Propagation under Constraints 8 1.3.2 Label Propagation with Preferences 10 1.3.3 Method St...

Community structure of networks provides comprehensive insight into their organizational structure and functional behavior. LPA is one of the most commonly adopted community detection algorithms with nearly linear time complexity. But it suffers from poor stability and occurrence of monster community due to the introduced randomize. We note that different community-oriented node roles impact th...

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text cla...

Label propagation is a popular graph-based semisupervised learning framework. So as to obtain the optimal labeling scores, the label propagation algorithm requires an inverse matrix which incurs the high computational cost ofO(n+cn), where n and c are the numbers of data points and labels, respectively. This paper proposes an efficient label propagation algorithm that guarantees exactly the sam...

In many applications non-metric distances are better than metric distances in reflecting the perceptual distances of human beings. Previous studies on non-metric distances mainly focused on supervised setting and did not consider the usefulness of unlabeled data. In this paper, we present probably the first study of label propagation on graphs induced from non-metric distances. The challenge he...

Article history: Received 24 December 2010 Available online 24 December 2011 Communicated by L. Yin

How can we tell when accounts are fake or real in a social network? And how can we tell which accounts belong to liberal, conservative or centrist users? Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily (”birds of a feather flock together”) or heterophily (”opposites attract”). One of the most widely...

This appendix presents additional discussion on several aspects of the proposed algorithm. Sec. A presents a modification of our algorithm which enables users to reflect local connectivity in link prediction. The remaining sections focus on the label propagation application. Sec. B and C discuss functionalities of active label acquisition and adding new images to the match graph while Sec. D di...