proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose

plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based

introduce new alternative methods specifically designed for citation graphs. We use the SCEAS system as a base platform to introduce these new methods and perform a generalized comparison of all methods. We also introduce an aggregate function for the generation of author ranking based on publication

This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to handle the special characteristics of hyperspectral images, namely high input dimension of pixels, low number of labeled samples, and spatial variability

Graph-based semi-supervised learning (SSL) algorithms have been successfully used to extract class-instance pairs from large unstructured and structured text collections. However, a careful comparison of different graph-based SSL algorithms on that task has been lacking. We compare three graph-based

graph-ranking-based method. It performs constrained reinforcements on a sentence graph, which unifies previous and current documents, to determine the salience of the sentences. The constraints ensure that the most salient sentences in current documents are updates to previous documents. Since

characterizations of a program have been shown to outperform models constructed using source code features. However, collecting performance counters requires running the program multiple times, and this “dynamic ” method of characterizing programs can be specific to inputs of the program. It would be preferable to

; they are set according to the tf.idf principal. Our method implements similarity propagation over graph edges using an iterative process. We evaluate the model using 4 different collections (TREC 2004 Novelty Track, CISI, Cranfield and Medline). We show that considering precision at 5 documents, GVC

shared coordinate frame. The method diers from other approaches by handling any number of sensors with only minimal constraints on their elds of view, producing a global solution that is better than any pairwise solution, and by simplifying the data collection process through automatic data association

Graph-based data mining (GDM) is the task of finding novel, useful, and understandable graph-theoretic patterns in a graph representation of data. Our approach to graph-based data mining, Subdue, focuses on identifying novel, but not necessarily most frequent, patterns in a graph representation