Conference item icon

Conference item

Graph-based semi-supervised and active learning for edge flows

Abstract:

We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constraints on the overall flows, such as (approximate) flow conservation. These constraints render our approach different from classical graph-based SSL for vertex labels, which posits that tightly connect...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1145/3292500.3330872

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-2426-6404
More from this funder
Name:
Army Research Office
Grant:
W911NF-19-1-0057
More from this funder
Name:
National Science Foundation
Grant:
DMS-1830274
More from this funder
Name:
European Research Council
Grant:
Marie Sklodowska-Curie grant: 702410
Publisher:
Association for Computing Machinery
Host title:
KDD '19 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Journal:
25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD ’19) More from this journal
Pages:
761-771
Publication date:
2019-07-25
Acceptance date:
2019-05-17
DOI:
ISBN:
9781450362016
Pubs id:
pubs:1003224
UUID:
uuid:ac947dc2-89da-4cf7-b652-dac4c7e8555d
Local pid:
pubs:1003224
Source identifiers:
1003224
Deposit date:
2019-07-16

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP