Many web-based application areas must infer label distributions starting from a small set of sparse, noisy labels. Previous work has shown that graph-based propagation can be very effective at finding the best label distribution across nodes, starting from partial information and a weightedconnection graph. In their work on video recommendations, Baluja et al. showed high-quality results using ...

This paper presents a comprehensive study about different methods which were used to detect the communities in networks. Most of the algorithms were failed to perform in all kind of networks i.e. some perform only in weighted networks, some in bi-partite networks and some in directed networks etc. Label propagation algorithm plays the most effective role in community detection as it takes much ...

Label propagation is one of the state-of-the-art methods for semi-supervised learning, which estimates labels by propagating label information through a graph. Label propagation assumes that data points (nodes) connected in a graph should have similar labels. Consequently, the label estimation heavily depends on edge weights in a graph which represent similarity of each node pair. We propose a ...

Graph-Based label propagation algorithms are popular in the state-of-the-art semi-supervised learning research. The key idea underlying this algorithmic family is to enforce labeling consistency between any two examples with a positive similarity. However, negative similarities or dissimilarities are equivalently valuable in practice. To this end, we simultaneously leverage similarities and dis...

Assigning several labels to digital data is becoming easier because we can perform it in a collaborative manner with Internet users. However, some suitable labels may be missed and may not be attached to the data leading to inaccuracies in classification. In this paper, we propose a novel graphbased multi-label classifier to support the multi-labeling task. The core process of our algorithm is ...

Many problems in computer science can be represented by a graph and reducedto a graph clustering or k-way partitioning problem. In the classical definition,a graph consists of nodes and edges which usually connect exactly two nodes.Hypergraphs are a generalization of graphs, where every edge can connect anarbitrary number of nodes. Recent results suggest that some problems i...

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation pr...

Label propagation has proven to be a fast method for detecting communities in complex networks. Recent work has also improved the accuracy and stability of the basic algorithm, however, a general approach is still an open issue. We propose different label propagation algorithms that convey two unique strategies of community formation, namely, defensive preservation and offensive expansion of co...