Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph benchmark datasets that are widely used by the community primarily focus on homogeneous node and edge attributes and are static. In this work, we present a variety of large scale, dynamic heterogeneous academic graphs to test the effectiveness of models developed for multi-step graph forecasting tasks. Our novel datasets cover both context and content information extracted from scientific publications across two communities -Artificial Intelligence (AI) and Nuclear Nonproliferation (NN). In addition, we propose a systematic approach to improve the existing evaluation procedures used in the graph forecasting models.
Consider the directions of the long-term financial policy of the organization, financial support, sources of financing, capital structure and dividend policy, planning and forecasting framework. Examples of business situations appropriate to the topics studied,there are questions forself-study, and a Glossary. For university students studying in the fields of study "Economics" and "Management". It is interesting to teachers of economic universities, as well as employees of the financial services business organizations
International Finance – an integral part and one of the most important areas of a market economy. They focus the problems of national and world economy, the development of which historically runs parallel and closely intertwined. As the internationalization of economic relations are increasing international flows of goods, services and especially capital and credit. This studybook is intended for students majoring in "Economics" and "Management". And can also be useful for teachers of economic universities and all interested in the problems of development of international relations.
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