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Title: Balancing between the Local and Global Structures (LGS) in Graph Embedding
We present a method for balancing between the Local and Global Structures ( L G S ) in graph embedding, via a tunable parame- ter. Some embedding methods aim to capture global structures, while others attempt to preserve local neighborhoods. Few methods attempt to do both, and it is not always possible to capture well both local and global information in two dimensions, which is where most graph drawing live. The choice of using a local or a global embedding for visualization depends not only on the task but also on the structure of the underly-ing data, which may not be known in advance. For a given graph, L G S aims to find a good balance between the local and global structure to preserve. We evaluate the performance of L G S with synthetic and real- world datasets and our results indicate that it is competitive with the state-of-the-art methods, using established quality metrics such as stress and neighborhood preservation. We introduce a novel quality metric, cluster distance preservation, to assess intermediate structure capture. All source-code, datasets, experiments and analysis are available online.  more » « less
Award ID(s):
2212130
NSF-PAR ID:
10493391
Author(s) / Creator(s):
; ;
Publisher / Repository:
Springer
Date Published:
Journal Name:
31st International Symposium on Graph Drawing and Network Visualization (GD)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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