Graph Data Mining: Algorithm, Security and Application
暫譯: 圖形數據挖掘：演算法、安全性與應用

Xuan, Qi, Ruan, Zhongyuan, Min, Yong

Graph Data Mining: Algorithm, Security and Application

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商品描述

Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining.

This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic - the security of graph data mining - and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.

商品描述(中文翻譯)

圖形數據因其能夠建模物件之間的任意關係而具有強大的能力，並且在生物資訊學、交通網絡、科學合作、全球資訊網和社交網絡等多個現實應用領域中被廣泛使用。圖形數據挖掘用於從圖形數據中發現有用的信息和知識。節點、連結及半結構形式的複雜性在計算任務方面帶來挑戰，例如節點分類、連結預測和圖形分類。在這個背景下，最近提出了包括圖嵌入（graph embedding）和圖神經網絡（graph neural networks）在內的各種先進技術，以提高圖形數據挖掘的性能。

本書提供了圖形數據挖掘方法的最新綜述。它針對當前的熱點話題——圖形數據挖掘的安全性——提出了一系列檢測方法，以識別圖形數據中的對抗樣本。此外，它還向讀者介紹了圖形增強（graph augmentation）和子圖網絡（subgraph networks），以進一步增強模型，即提高其準確性和穩健性。最後，本書描述了這些先進技術在各種場景中的應用，例如交通網絡、社交和技術網絡以及區塊鏈。

作者簡介

Qi Xuan is a Professor at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, graph data mining, cyberspace security, and deep learning. He has published more than 50 papers in leading journals and conferences, including IEEE TKDE, IEEE TIE, IEEE TNSE, ICSE, and FSE. He is the reviewer of the journals such like IEEE TKDE, IEEE TIE, IEEE TII, and IEEE TNSE.

Zhongyuan Ruan is a lecturer at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, such as epidemic and information spreading in complex networks, and traffic networks. He has published more than 20 papers in journals such as Physical Review Letters, Physical Review E, Chaos, Scientific Reports, and Physica A.

Yong Min is an Associate Professor at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His research interests include social network analysis, computational communication, and artificial intelligence algorithms. He was named an Excellent Young Teacher of Zhejiang University of Technology. He has hosted and participated in more than ten projects, including those by national and provincial natural science foundations. He has also published over 30 papers, including two in the leading journal Nature and Science, and he holds more than three patents.

作者簡介(中文翻譯)

詹啟軒是中國杭州浙江工業大學網絡空間安全研究所的教授。他目前的研究興趣包括網絡科學、圖數據挖掘、網絡空間安全和深度學習。他在IEEE TKDE、IEEE TIE、IEEE TNSE、ICSE和FSE等頂尖期刊和會議上發表了50多篇論文。他是IEEE TKDE、IEEE TIE、IEEE TII和IEEE TNSE等期刊的審稿人。

阮中原是中國杭州浙江工業大學網絡空間安全研究所的講師。他目前的研究興趣包括網絡科學，例如在複雜網絡中的疫情和信息傳播，以及交通網絡。他在《物理評論快報》、《物理評論E》、《混沌》、《科學報告》和《物理A》等期刊上發表了20多篇論文。

閔勇是中國杭州浙江工業大學網絡空間安全研究所的副教授。他的研究興趣包括社交網絡分析、計算通信和人工智慧算法。他被評為浙江工業大學的優秀青年教師。他主持和參與了十多個項目，包括國家和省級自然科學基金的項目。他還發表了30多篇論文，其中兩篇發表在頂尖期刊《自然》和《科學》上，並擁有三項以上的專利。

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