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Comparison of influence measures on structural changes focused on node functions

Published: 11 December 2015 Publication History

Abstract

The structures of some real-world networks are dynamic in nature as time goes by. These changes consist of the addition or deletion of nodes or links and the rewiring of links. Even if link rewiring occurs, the influence degree tends to differ depending on the location in which it occurs, the nature of the nodes, and so forth. In this paper, by quantifying the influence degree of each node, we attempt to extract the influential structural changes wherein each node in a large population changes its function. Concretely, we define the node function as the PageRank convergence curve of the node and the influence degree affecting the node as distance based on a correlation coefficient of convergence curves before and after change occurs. We then propose the Structural Change Influence Measure (SCIM), which is the average value of the influence degree of all nodes. Based on experimental evaluation using several synthetic and real networks, we found five promising properties of our proposed measure. Our method indicates a higher value for changes in: 1) number of link rewirings; 2) concentrated link deleting; 3) link addition between distant nodes; 4) link addition between important and unimportant nodes; and 5) link deletion between communities.

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Cited By

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  • (2022) Identifying the Top- k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation IEEE Access10.1109/ACCESS.2022.321304410(107809-107845)Online publication date: 2022
  • (2022)Predicting stimulation index of information transmissions by local structural features in social networksSocial Network Analysis and Mining10.1007/s13278-022-00865-012:1Online publication date: 27-Feb-2022

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cover image ACM Other conferences
iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services
December 2015
704 pages
ISBN:9781450334914
DOI:10.1145/2837185
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 11 December 2015

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Author Tags

  1. PageRank
  2. convergence curve
  3. influence measure
  4. network structural change

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  • (2022) Identifying the Top- k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation IEEE Access10.1109/ACCESS.2022.321304410(107809-107845)Online publication date: 2022
  • (2022)Predicting stimulation index of information transmissions by local structural features in social networksSocial Network Analysis and Mining10.1007/s13278-022-00865-012:1Online publication date: 27-Feb-2022

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