In Chaps. 3–5, the structural balance theory of signed digraphs has been incorporated into the disagreement behavior analysis of signed networks, where the simultaneous existence of the cooperative and antagonistic interactions among agents can be accommodated. As shown in Chap. 3, once the strong connectivity is given, the dynamic behaviors of signed networks can be determined directly by the structural balance and unbalance of their associated signed digraphs. This result, however, does not apply in the absence of the strong connectivity property any longer, as shown in Chaps. 4 and 5. A main reason is that the classic structural balance theory targets at capturing the sign patterns of the whole signed networks, making the direct use of it not able to clearly reveal the specific disagreement behaviors of local agents. With these observations, the global perspective of the structural balance theory seems less effective for studying the disagreement behaviors of signed networks. In this chapter, a notion of structurally balanced nodes is introduced, with the focus on developing the structural balance property of “local nodes” instead of the global signed digraphs. It is shown that the agents associated with structurally balanced nodes play a dominant role in determining the disagreement behaviors of signed networks. Moreover, such an extension of structural balance is applied to distinguish the roles of all agents to create a hierarchical structure decomposition of signed networks, which helps to establish the local disagreement characterization of each agent and reveal more specific disagreement behaviors for signed networks. Particularly, regarding the interval bipartite consensus of quasi-strongly connected signed networks, the impact index of root agents can be calculated by directly counting the number of structurally balanced agents. Illustrative examples are given to verify the established results for the local disagreement characterization of signed networks.


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    Titel :

    Local Disagreement Characterization


    Weitere Titelangaben:

    Intelligent Control & Learning Systems


    Beteiligte:
    Meng, Deyuan (Autor:in) / Du, Mingjun (Autor:in) / Wu, Yuxin (Autor:in)


    Erscheinungsdatum :

    03.12.2022


    Format / Umfang :

    23 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch