Task graphs and their equivalents have proved to be a valuable abstraction for representing the execution of parallel programs in a number of different applications. Perhaps the most widespread use of task graphs has been for performance modeling of parallel programs, including quantitative analytical models [3, 19, 25, 26, 27], theoretical and abstract analytical models [14], and program simul...

Word Sense Induction (WSI) is an unsupervised approach for learning the multiple senses of a word. Graph-based approaches to WSI frequently represent word co-occurrence as a graph and use the statistical properties of the graph to identify the senses. We reinterpret graph-based WSI as community detection, a well studied problem in network science. The relations in the co-occurrence graph give r...

Graph centrality has been extensively applied in Social Network Analysis to model the interaction of actors and the information flow inside a graph. In this paper, we investigate the usage of graph centralities in the Shape Matching task. We create a graph-based representation of a shape and describe this graph by using different centrality measures. We build a Naive Bayes classifier whose inpu...

this paper examines the theoretical rationales and practical aspects of task-based language teaching (tblt) with particular reference to research findings in efl/esl contexts. the definitional scope of the term ‘task’, polarizations in terms of task vs. non-task, and its relation to different language teaching approaches have engendered conceptual and methodological ambiguities.  moreover, fact...

The task of community detection in a graph formalizes the intuitive task of grouping together subsets of vertices such that vertices within clusters are connected tighter than those in disparate clusters. This paper approaches community detection in graphs by constructing Markov random walks on the graphs. The mixing properties of the random walk are then used to identify communities. We use co...

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...

We present an information visualization investigation into the quantum annealing domain. We conduct original research to characterize the data and task abstractions in this field. The task and data abstractions that we explore are used as the basis of a new representation of the Chimera graph, an important part of the current quantum annelaing process. Our redesign reduces the visual clutter of...

In this paper, we present deep attributes residue graph algorithm (DARG), a novel model for learning deep representations of graph. The algorithm can discover clusters by taking into consideration node relevance. DARG does so by first learns attributes relevance and cluster deep representations of vertices appearing in a graph, unlike existing work, integrates content interactions of the nodes ...

Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature vectors constructed from different global topological attributes, as well...

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...