In this paper, we address the scalability issue plaguing graph-based semi-supervised learning via a small number of anchor points which adequately cover the entire point cloud. Critically, these anchor points enable nonparametric regression that predicts the label for each data point as a locally weighted average of the labels on anchor points. Because conventional graph construction is ineffic...

In this paper, we present a new streaming model for Graph-parallel community detection in dynamic social network using Spark GraphX tools on clouds. Two graph algorithms: SLP (streaming label propagation) and SGA (streaming genetic algorithm), are streamlined for Graphparallel execution in the SparkX execution environment. We developed a new streaming pipeline model for GraphXparallel execution...

It is increasingly common to find real-life data represented as networks of labeled, heterogeneous entities. To query these networks, one often needs to identify the matches of a given query graph in a (typically large) network modeled as a target graph. Due to noise and the lack of fixed schema in the target graph, the query graph can substantially differ from its matches in the target graph i...

In this paper, a space partition method called “Label Constrained Graph Partition” (LCGP) is presented to solve the Sample-InterweavingPhenomenon in the original space. We first divide the entire training set into subclasses by means of LCGP, so that the scopes of subclasses will not overlap in the original space. Then “Most Discriminant Subclass Distribution” (MDSD) criterion is proposed to de...

The aim is to predict the labeling of the vertices of a graph. The graph is given. A trial sequence of (vertex,label) pairs is then incrementally revealed to the learner. On each trial a vertex is given and the learner predicts a label and then the true label is returned. The learner’s goal is to minimize mistaken predictions. We propose to solve the problem by the method of best approximation....

We approximate the k-label Markov random field optimization by a single binary (s−t) graph cut. Each vertex in the original graph is replaced by only ceil(log2(k)) new vertices and the new edge weights are obtained via a novel least squares solution approximating the original data and label interaction penalties. The s− t cut produces a binary “Gray” encoding that is unambiguously decoded into ...

We derive a novel semi-supervised learning method that propagates label information as a symmetric, anisotropic diffusion process (SADP). Since the influence of label information is strengthened at each iteration, the process is anisotropic and does not blur the label information. We show that SADP converges to a closed form solution by proving its equivalence to a diffusion process on a tensor...

We present an adaptation of the recently proposed graph-shifts algorithm for labeling MRF problems from low-level vision. Graph-shifts is an energy minimization algorithm that does labeling by dynamically manipulating, or shifting, the parent-child relationships in a hierarchical decomposition of the image. Graph-shifts was originally proposed for labeling using relatively small label sets (e.g...

Label propagation is a popular semi-supervised learning technique that transfers information from labeled examples to unlabeled examples through a graph. Most label propagation methods construct a graph based on example-to-example similarity, assuming that the resulting graph connects examples that share similar labels. Unfortunately, examplelevel similarity is sometimes badly defined. For inst...