The Network of Tasks (NOT) model allows adaptive node programs written in a variety of parallel languages to be connected together in an almost acyclic task graph. The main diierence between NOT and other task graphs is that it is designed to make the performance of the graph predictable from knowledge of the performance of the component node programs and the visible structure of the graph. It ...

Effectively visualizing large graphs is challenging. Capturing the statistical properties of these large graphs is also difficult. Sampling algorithms, developed to more feasibly observe and analyze large graphs, are indispensable for this task. Many sampling approaches for graph simplification have been proposed. These methods can be grouped into three categories: node sampling, edge sampling,...

We describe the approach to event extraction which the JULIELab Team from FSU Jena (Germany) pursued to solve Task 1 in the “BioNLP’09 Shared Task on Event Extraction”. We incorporate manually curated dictionaries and machine learning methodologies to sort out associated event triggers and arguments on trimmed dependency graph structures. Trimming combines pruning irrelevant lexical material fr...

We propose representing a text corpus as a labeled directed graph, where nodes represent words and weighted edges represent the syntactic relations between them, as derived by dependency parsing. Given this graph, we adopt a graph-based similarity measure based on random walks to derive a similarity measure between words, and also use supervised learning to improve the derived similarity measur...

A centralized scheduler can become a bottleneck for placing the tasks of many-task application on heterogeneous cloud resources. We have previously demonstrated that decentralized vector scheduling approach based performance measurements be used successfully this task placement scenario. then extended to latency measurements. Each node collects from its neighbors an overlay graph, measures comm...

We describe a framework for representing email as well as meeting information as a joint graph. In the graph, documents and meeting descriptions are connected via other nontextual objects representing the underlying structure-rich data. This framework integrates content, social networks and a timeline in a structural graph. Extended similarity metrics for objects embedded in the graph can be de...

Mobile ad hoc networks (MANETs) are inherently prone to network partitioning and link failures due to node mobility. Distributed applications executed on such environments must cope with these events. In this dissertation, a novel application execution framework and model called a task graph is presented that enables a class of distributed applications suitable for MANETs. The framework allows ...

In this paper, we propose a mathematical framework for automated bug localization. This framework can be briefly summarized as follows. A program execution can be represented as a rooted acyclic directed graph. We define an execution snapshot by a cut-set on the graph. A program state can be regarded as a conjunction of labels on edges in a cut-set. Then we argue that a debugging task is a prun...

Graph theory defines a graph as a set of nodes (in this study, regions of interest, ROIs) and edges (functional connections). The present study aims to explore the topological properties of the Default Mode Network (DMN) in the framework of graph theory and the alterations induced on it by a cognitive task, with respect to the resting condition. The principal finding of the present study is tha...

The efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection between entities. The classic choice for this metric is usually a distance me...