Graph similarity metrics for assessing temporal changes in attack surface of dynamic networks

2017 ◽  
Vol 64 ◽  
pp. 16-43 ◽  
Author(s):  
Ghanshyam S. Bopche ◽  
Babu M. Mehtre
2021 ◽  
Vol 9 ◽  
pp. 1425-1441
Author(s):  
Juri Opitz ◽  
Angel Daza ◽  
Anette Frank

Abstract Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: Some emphasize speed, while others make the alignment of graph structures explicit, at the price of a costly alignment step. In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses. Specifically, our new metrics are able to match contextualized substructures and induce n:m alignments between their nodes. Furthermore, we introduce a Benchmark for AMR Metrics based on Overt Objectives (Bamboo), the first benchmark to support empirical assessment of graph-based MR similarity metrics. Bamboo maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric’s robustness against meaning-altering and meaning- preserving graph transformations. We show the benefits of Bamboo by profiling previous metrics and our own metrics. Results indicate that our novel metrics may serve as a strong baseline for future work.


2006 ◽  
Vol 2006 ◽  
pp. 281-282
Author(s):  
J.J. Gleysteen
Keyword(s):  

Author(s):  
Soowon Chang ◽  
Takahiro Yoshida ◽  
Robert Brent Binder ◽  
Yoshiki Yamagata ◽  
Daniel Castro-Lacouture

2007 ◽  
Vol 35 (4) ◽  
pp. 1563-1571 ◽  
Author(s):  
I. Bauer ◽  
S. Mladenovic Drinic ◽  
G. Drinić ◽  
D. Ignjatović Micić

2019 ◽  
Vol 78 (14) ◽  
pp. 1249-1261
Author(s):  
O. Rubel ◽  
S. K. Abramov ◽  
V. V. Abramova ◽  
V. V. Lukin

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