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pro vyhledávání: '"Lee, John Boaz"'
Publikováno v:
ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 14, No. 5, Article 63 (August 2020), 37 pages
Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connections inside the set than outside. Roles based on structural similarity an
Externí odkaz:
http://arxiv.org/abs/1908.08572
Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Such temporal networks (or edge streams) consist of a sequence of timestamped edges and are seemingly ubiquitous. Despite the importance of accuratel
Externí odkaz:
http://arxiv.org/abs/1904.06449
Following the success of deep convolutional networks in various vision and speech related tasks, researchers have started investigating generalizations of the well-known technique for graph-structured data. A recently-proposed method called Graph Con
Externí odkaz:
http://arxiv.org/abs/1809.07697
Graph-structured data arise naturally in many different application domains. By representing data as graphs, we can capture entities (i.e., nodes) as well as their relationships (i.e., edges) with each other. Many useful insights can be derived from
Externí odkaz:
http://arxiv.org/abs/1807.07984
Autor:
Ahmed, Nesreen K., Rossi, Ryan, Lee, John Boaz, Willke, Theodore L., Zhou, Rong, Kong, Xiangnan, Eldardiry, Hoda
Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and
Externí odkaz:
http://arxiv.org/abs/1802.02896
Autor:
Ahmed, Nesreen K., Rossi, Ryan A., Zhou, Rong, Lee, John Boaz, Kong, Xiangnan, Willke, Theodore L., Eldardiry, Hoda
Graphs (networks) are ubiquitous and allow us to model entities (nodes) and the dependencies (edges) between them. Learning a useful feature representation from graph data lies at the heart and success of many machine learning tasks such as classific
Externí odkaz:
http://arxiv.org/abs/1710.09471
Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph features. In a graphlet-based approach, for instance, the entire graph is
Externí odkaz:
http://arxiv.org/abs/1709.06075
Autor:
Ahmed, Nesreen K., Rossi, Ryan A., Zhou, Rong, Lee, John Boaz, Kong, Xiangnan, Willke, Theodore L., Eldardiry, Hoda
Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to
Externí odkaz:
http://arxiv.org/abs/1709.04596
Autor:
Lee, John Boaz, Teknomo, Kardi
The widespread adoption of smartphones in recent years has made it possible for us to collect large amounts of traffic data. Special software installed on the phones of drivers allow us to gather GPS trajectories of their vehicles on the road network
Externí odkaz:
http://arxiv.org/abs/1609.02409
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