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LEARNING SIGNED GRAPHS FROM DATA

Citation Author(s):
Thomas Dittrich
Submitted by:
Gerald Matz
Last updated:
14 May 2020 - 5:04am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters:
Gerald Matz
Paper Code:
SPTM-P1.10
 

Signed graphs have recently been found to offer advantages over unsigned graphs in a variety of tasks. However, the problem of learning graph topologies has only been considered for the unsigned case. In this paper, we propose a conceptually simple and flexible approach to signed graph learning via signed smoothness metrics. Learning the graph amounts to solving a convex optimization problem, which we show can be reduced to an efficiently solvable quadratic problem. Applications to signal reconstruction and clustering corroborate the effectiveness of the proposed method.

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