E389-03 - Forschungsbereich Signal Processing E389 - Institute of Telecommunications
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Published in:
2022 56th Annual Asilomar Conference on Signals, Systems, and Computers
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ISBN:
9781665459068
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Date (published):
1-Jan-2022
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Event name:
2022 56th Annual Asilomar Conference on Signals, Systems, and Computers
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Event date:
31-Oct-2022 - 2-Nov-2022
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Event place:
Pacific Grove, CA, United States of America (the)
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Number of Pages:
5
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Publisher:
IEEE, Piscataway
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Peer reviewed:
Yes
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Keywords:
Graph signal processing; clustering, graph learning
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Abstract:
We introduce the novel concept of signature graphs. Contrary to conventional unsigned and signed graphs, signature graphs capture partial similarity/agreement or dissimilarity/disagreement between a collection of objects in an efficient and intuitive manner. We discuss basic properties of this type of graphs and establish a connection to multilayer graphs. Furthermore, we define the notion of balancedness for signature graphs and derive several equivalent conditions for balancedness. Finally, we develop a coherent framework for learning a signature graph from given data and for clustering the nodes of signature graphs. The latter has a complexity that scales only logarithmically in the number of clusters.
