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Deep collective inference.
~
Moore, John A.
Deep collective inference.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Deep collective inference./
Author:
Moore, John A.
Description:
1 online resource (54 pages)
Notes:
Source: Masters Abstracts International, Volume: 56-03.
Subject:
Computer science. -
Online resource:
click for full text (PQDT)
ISBN:
9781369643121
Deep collective inference.
Moore, John A.
Deep collective inference.
- 1 online resource (54 pages)
Source: Masters Abstracts International, Volume: 56-03.
Thesis (M.S.)--Purdue University, 2016.
Includes bibliographical references
Collective inference is widely used to improve classification in network datasets.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781369643121Subjects--Topical Terms:
573171
Computer science.
Index Terms--Genre/Form:
554714
Electronic books.
Deep collective inference.
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Deep collective inference.
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1 online resource (54 pages)
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Source: Masters Abstracts International, Volume: 56-03.
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Adviser: Jennifer Neville.
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Thesis (M.S.)--Purdue University, 2016.
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Includes bibliographical references
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Collective inference is widely used to improve classification in network datasets.
520
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However, despite recent advances in deep learning and the successes of recurrent neural.
520
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networks (RNNs), researchers have only just recently begun to study how to apply.
520
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RNNs to heterogeneous graph and network datasets. There has been recent work on.
520
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using RNNs for unsupervised learning in networks (e.g., graph clustering, node embedding).
520
$a
and for prediction (e.g., link prediction, graph classification), but there has.
520
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been little work on using RNNs for node-based relational classification tasks. In this.
520
$a
paper, we provide an end-to-end learning framework using RNNs for collective inference.
520
$a
Our main insight is to transform a node and its set of neighbors into an.
520
$a
unordered sequence (of varying length) and use an LSTM-based RNN to predict the.
520
$a
class label as the output of that sequence. We develop a collective inference method,
520
$a
which we refer to as Deep Collective Inference (DCI), that uses semi-supervised learning.
520
$a
in partially-labeled networks and two label distribution correction mechanisms.
520
$a
for imbalanced classes. We compare to several alternative methods on seven network.
520
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datasets. DCI achieves up to a 12% reduction in error compared to the best.
520
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alternative and a 25% reduction in error on average over all methods, for all label.
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proportions.
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Electronic reproduction.
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Ann Arbor, Mich. :
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ProQuest,
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2018
538
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Mode of access: World Wide Web
650
4
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Computer science.
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573171
655
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Electronic books.
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554714
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ProQuest Information and Learning Co.
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Purdue University.
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Computer Sciences.
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1179390
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10248759
$z
click for full text (PQDT)
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