《计算机信息》杂志发表高质量的论文,扩大了运筹学和计算的范围,寻求有关理论、方法、实验、系统和应用方面的原创研究论文、新颖的调查和教程论文,以及描述新的和有用的软件工具的论文。官网链接:https://pubsonline.informs.org/journal/ijoc

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Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics system, learning molecular fingerprints, predicting protein interface, and classifying diseases require that a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures, like the dependency tree of sentences and the scene graph of images, is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. Unlike standard neural networks, graph neural networks retain a state that can represent information from its neighborhood with an arbitrary depth. Although the primitive graph neural networks have been found difficult to train for a fixed point, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful learning with them. In recent years, systems based on graph convolutional network (GCN) and gated graph neural network (GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a detailed review over existing graph neural network models, systematically categorize the applications, and propose four open problems for future research.

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Terrorism is one of the most serious life-challenging threat facing humanity around the world. The activities of terrorist organization threatens peace, disrupts progress and halts all-round development of any nation. Terrorist activities in Nigeria in the last decades has negatively affected the economic growth and has drastically reduced the possibilities of foreign investments in Nigeria. In this paper, statistical and inferential insights are applied to the terrorist activities in Nigeria between 1970 to 2019. Using the Global Terrorism Database (GTD), insights are made on the occurrences of terrorist attacks, the localities of target and the successful and unsuccessful rates of such attacks. The Apriori algorithm is also used in this paper to draw hidden patterns from the GTD in order to aid in the generation of strong rules through database mining, resulting in relevant insights. This understanding of terrorist activities will provide security agencies with the needed information to be one step ahead of terrorist, hence assisting in curbing terrorism in Nigeria.

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Terrorism is one of the most serious life-challenging threat facing humanity around the world. The activities of terrorist organization threatens peace, disrupts progress and halts all-round development of any nation. Terrorist activities in Nigeria in the last decades has negatively affected the economic growth and has drastically reduced the possibilities of foreign investments in Nigeria. In this paper, statistical and inferential insights are applied to the terrorist activities in Nigeria between 1970 to 2019. Using the Global Terrorism Database (GTD), insights are made on the occurrences of terrorist attacks, the localities of target and the successful and unsuccessful rates of such attacks. The Apriori algorithm is also used in this paper to draw hidden patterns from the GTD in order to aid in the generation of strong rules through database mining, resulting in relevant insights. This understanding of terrorist activities will provide security agencies with the needed information to be one step ahead of terrorist, hence assisting in curbing terrorism in Nigeria.

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