| 書目名稱 | Individual and Collective Graph Mining | | 副標(biāo)題 | Principles, Algorith | | 編輯 | Danai Koutra,Christos Faloutsos | | 視頻video | http://file.papertrans.cn/464/463724/463724.mp4 | | 叢書名稱 | Synthesis Lectures on Data Mining and Knowledge Discovery | | 圖書封面 |  | | 描述 | Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company?This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas:..Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities...Collec | | 出版日期 | Book 2018 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01911-1 | | isbn_softcover | 978-3-031-00783-5 | | isbn_ebook | 978-3-031-01911-1Series ISSN 2151-0067 Series E-ISSN 2151-0075 | | issn_series | 2151-0067 | | copyright | Springer Nature Switzerland AG 2018 |
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