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...

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 ...

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...

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...

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...

to evaluate off-label and off-nccn guidelines uses of antineoplastic drugs in a major chinese hospital.totally 1122 patients were selected from july to december 2011. then, the off-label and off-nccn guidelines uses of antineoplastic drugs were analyzed.in 798 of 1122 patients (71.12%), drugs were used for off-label. in 317 of 1122 patients (28.25%), the drugs were prescribed for off-label and ...

Given a graph G = (V,E) with non-negative edge lengths whose vertices are assigned a label from L = {λ1, . . . , λl}, we construct a compact distance oracle that answers queries of the form: “What is δ(v, λ)?”, where v ∈ V is a vertex in the graph, λ ∈ L a vertex label, and δ(v, λ) is the distance (length of a shortest path) between v and the closest vertex labeled λ in G. We formalize this nat...