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Mining Large Graphs.

Zhao, Yuchen.

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Mining Large Graphs.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Mining Large Graphs./
作者:	Zhao, Yuchen.
面頁冊數:	163 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.
Contained By:	Dissertation Abstracts International74-12B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3573410
ISBN:	9781303435850

Mining Large Graphs.
Zhao, Yuchen.

Mining Large Graphs. - 163 p.

Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.

Thesis (Ph.D.)--University of Illinois at Chicago, 2013.

Recently, there is an increasing need for mining graphs with the rapidly growing social networks, Internet applications and communication networks. Among all these real-world applications, graphs are ubiquitous and contain tremendous useful information in every aspect. In this thesis, we focus on studying graph structures and apply the knowledge from graph structures to a number of fundamental data mining tasks.

ISBN: 9781303435850Subjects--Topical Terms:

626642
Computer Science.

Mining Large Graphs.
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500 $a Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.
500 $a Adviser: Philip S. Yu.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2013.
520 $a Recently, there is an increasing need for mining graphs with the rapidly growing social networks, Internet applications and communication networks. Among all these real-world applications, graphs are ubiquitous and contain tremendous useful information in every aspect. In this thesis, we focus on studying graph structures and apply the knowledge from graph structures to a number of fundamental data mining tasks.
520 $a The graph structured mining tasks are very challenging due to the following reasons. First, the number of possible edges scales up quadratically with the number of nodes. Thus, the number of edges of a graph data set can be extremely large and it is difficult to mine useful knowledge from such massive graph structures. (2) Many graphs are naturally associated with many useful side information. However, such information can be noisy and can be difficult to incorporate into the mining model. How to use the side information and auxiliary attributes in the mining approaches in a meaningful way is non-trivial. (3) Different from traditionally data mining techniques, the graph structures are complex and lack of existing features in the graph data. Traditional learning techniques focus on mining in a fixed feature space. However, graphs are not directly represented in a meaningful feature space. For social network applications, finding the right features for the mining tasks is even a more challenging task, since most features of graph structures are weak signals and cannot be directly used in the model.
520 $a Motivated by these challenges, in this thesis, we propose a hash-based compression framework to efficiently and effectively cluster graph objects in the stream scenario. We then extend it to the graph clustering problem with side information. We propose a novel optimization framework DMO, which can dynamically optimize the weights of graph distance and side information distance metrics. The hash-based compression framework consumes constant storage spaces and the mining process can be scalable to massive graphs with side attributes. We then study the graph structures from another perspective, i.e., positive and unlabeled learning in graphs. We derive an evaluation criterion to estimate the dependency between structural features and labels, and then propose an integrated approach that concurrently updates both graph feature selection and class label assignment. By using structural features from graph objects, the experimental results shows that the proposed integrated framework significantly outperforms the previous methods.
520 $a As graph structures are very useful for understanding the nature of graphs, we further extend our analysis to online social networks. We explore five social principles and concepts that represent a variety of network characteristics and quantify their relations with social roles and statuses. We propose a novel probabilistic model SRS, which can integrate both the local social factors of individual users and network influence via neighbors in a principled way.
590 $a School code: 0799.
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710 2 $a University of Illinois at Chicago. $3 1020478
773 0 $t Dissertation Abstracts International $g 74-12B(E).
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791 $a Ph.D.
792 $a 2013
793 $a English
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