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Inference in Heterogeneous Networks.

Li, Yuan.

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Inference in Heterogeneous Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inference in Heterogeneous Networks./
Author:	Li, Yuan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	97 p.
Notes:	Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
Contained By:	Dissertation Abstracts International80-03B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10825217
ISBN:	9780438550728

Inference in Heterogeneous Networks.
Li, Yuan.

Inference in Heterogeneous Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 97 p.

Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.

Thesis (Ph.D.)--Northwestern University, 2018.

Last two decades have seen a surge of interests in approaches that leverage network structure in machine learning models. For many networks, not only the connections of the network but also the network attributes, such as node attributes and dyadic attributes, are observed. This heterogeneity in networks raises new challenges for the inference problem in networks.

ISBN: 9780438550728Subjects--Topical Terms:

517247
Statistics.

Inference in Heterogeneous Networks.
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020 $a 9780438550728
035 $a (MiAaPQ)AAI10825217
035 $a (MiAaPQ)northwestern:14208
035 $a AAI10825217
040 $a MiAaPQ $c MiAaPQ
100 1 $a Li, Yuan. $3 1682511
245 1 0 $a Inference in Heterogeneous Networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 97 p.
500 $a Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
500 $a Adviser: Wenxin Jiang.
502 $a Thesis (Ph.D.)--Northwestern University, 2018.
520 $a Last two decades have seen a surge of interests in approaches that leverage network structure in machine learning models. For many networks, not only the connections of the network but also the network attributes, such as node attributes and dyadic attributes, are observed. This heterogeneity in networks raises new challenges for the inference problem in networks.
520 $a This dissertation discusses how to handle the heterogeneous networks for different ma- chine learning applications, namely community detection, node classification, and node representation learning. For community detection in network with node attributes, we introduce a mathematical approach that combines topology information and nodes at- tributes. The algorithm explores the correlation between node attributes and community assignment, and uses the diversity of dyadic attributes induced by different types of nodes to improve performance as well. We also study node classification problem in a transaction network, where rich information of node and edge is available, within Markov random field framework. We present a novel algorithm that automatically learns node prior and edge potential in the Markov random field, hence results in better classification. Finally, we generalize deepwalk to incorporate the dyadic attributes in network representation learning by biasing the random walk sampling procedure in deepwalk. The algorithm learns the sampling weights in a data driven manner and constructs a proper proximity measure based on the dyadic attributes.
590 $a School code: 0163.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a Northwestern University. $b Statistics. $3 1673808
773 0 $t Dissertation Abstracts International $g 80-03B(E).
790 $a 0163
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10825217