This work investigates whether the development of ideas in writing can be captured by graph properties derived from the text. Focusing on student essays, we represent the essay as a graph, and encode a variety of graph properties including PageRank as features for modeling essay scores related to quality of development. We demonstrate that our approach improves on a stateof-the-art system on th...

In this paper the questions of the definition of the centers optimum allocation in fuzzy transportation networks are observed by the minimax criterion. It is supposed that the information received from GIS is presented like a fuzzy graph. In this case the task of the definition of the centers optimum allocation transforms into the task of the definition of the graph vitality fuzzy set. The meth...

This study aims to increase the productivity of grid systems by an improved scheduling method. A brief overview and analysis of the main scheduling methods in grid systems are presented. A method for increasing efficiency by optimizing the task graph structure considering the grid system node structure is proposed. Task granularity (the ratio between the amount of computation and transferred da...

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...

We consider the problem of scheduling static task graphs by using Cilk, a C-based runtime system for multithreaded parallel programming. We assume no pre-emption of task execution and no prior knowledge of the task execution times. Given a task graph G, the output of the scheduling algorithm is a Cilk program P which, when executed, initiates the tasks in consistence with the precedence require...

Graph convolutional neural networks have received a lot of attention in various tasks dealing with graph data by aggregating information from neighboring nodes and passing node information. Many recent studies looked at the impact topological features on classification altering aggregation based degree values or incorporating analysis into networks; however, itself has many characteristics comp...

Graph neural network (GNN), as a widely used deep learning model in processing graph-structured data, has attracted numerous studies to apply it the link prediction task. In these studies, observed edges are utilized positive samples, and unobserved randomly sampled negative ones. However, there problems sampling samples. First, some missing that existing network. Second, can be easily distingu...

In this paper the questions of defining the optimum allocation of centers in fuzzy transportation networks are observed by the minimax criterion. It is supposed that the information received from the geographical information system is presented as a fuzzy graph. In this case the task of defining optimum allocation of the centers transforms into the task of defining the fuzzy set of graph bases....