We present a novel framework to represent sets of time-varying signals as dynamic graphs using the non-negative kernel (NNK) graph construction. We extend the original NNK framework to allow explicit delays as part of the graph construction, so that unlike in NNK, two nodes can be connected with an edge corresponding to a non-zero time delay, if there is higher similarity between the signals after shifting one of them. We also propose to characterize the similarity between signals at different nodes using the node degree and clustering coefficients of their respective visibility graphs. Graph edges that can representing temporal delays, we provide a new perspective that enables us to see the effect of synchronization in graph construction for time-series signals. For both temperature and EEG datasets, we show that our proposed approach can achieve sparse and interpretable graph representations. Furthermore, the proposed method can be useful in characterizing different EEG experiments using sparsity.
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An Adjacency Matrix Approach to Delay Analysis in Temporal Networks
Wireless communications networks are often mod- eled as graphs in which the vertices represent wireless devices and the edges represent the communication links between them. However, graphs fail to capture the time-varying nature of wire- less networks. Temporal networks are graphs in which the sets of nodes or edges are time-varying. We consider the most common case, in which the set of nodes is fixed but the presence of edges changes over time. Most previous work on analyzing temporal networks has focused on summary measures that combine the contributions of different paths by using different weights for paths with different delays. Such summary measures are efficient to compute but may lose valuable information about the temporal behavior of the network. We propose techniques that characterize the delays of all paths between nodes in temporal networks. We then apply these techniques to identify dominant patterns in the temporal paths connecting nodes. Example temporal networks are used to illustrate these phenomena, and we consider implications to wireless networks.
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- Award ID(s):
- 1642973
- NSF-PAR ID:
- 10046905
- Date Published:
- Journal Name:
- MILCOM IEEE Military Communications Conference
- ISSN:
- 2155-7578
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Dynamic or temporal networks enable representation of time-varying edges between nodes. Conventional adjacency-based data structures used for storing networks such as adjacency lists were designed without incorporating time and can thus quickly retrieve all edges between two sets of nodes (a
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
