Abstract
Information dissemination in online social networks determines the broad contours of how users interact within a platform and is often seen in the form of percolation of text, audio, and video messages, in addition to likes, dislikes, and mentions. As this information dissemination forms the bedrock of all major events happening in a given social network like news diffusion, virality of products, ad campaigns, friendship patterns, educational enlightenment, researchers have become active in finding the answer to various questions related to information diffusion. One of the important aspects of information diffusion is detection of seed nodes; nodes that flood a given social network and are the actual sources/creators of information emanating from a social network. These seed nodes are detected using a variety of centrality measures such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank centrality; but all of them suffer from explicit specification of seed node limit k or implicit threshold calculation for seed limit at high complexities and thus have no self-classifying power to detect seed and non-seeds. In this paper, we identify these seed nodes using a newly proposed degree centrality ‘netdegree’ by utilizing the breadth first search propagation model. This novel approach uses the principle of outdegree of a given node in a network to find the netdegree and if it has a positive value, the node is treated as a seed node; otherwise, it is a non-seed node. We tested our netdegree centrality on five large online social network datasets in addition to three small benchmark datasets, and the findings for detection of seed nodes were promising. We compared our results with the existing centrality measures, and the comparison makes it clear that our approach is self-classifying and does not require any implicit or explicit seed limit parameter. Further, it is quite clear that that the proposed study is scalable to majority of online social networks in addition to being computationally time efficient when compared with other state of the art centrality-measure-based algorithms.
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Data availability statement
All the datasets used in this research are freely available on the internet. The small benchmarking datasets have been taken from Pajek dataset repository http://vlado.fmf.uni-lj.si/pub/networks/data/esna/default.htm, and all other datasets have been taken from SNAP dataset repository https://snap.stanford.edu/data/.
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All the three authors have worked for this manuscript; their contributions are as: Aaquib Hussain Ganai has identified the research problem, designed, developed and implemented the overall model for the identified research and algorithms for the paper. He has done all the comparative studies presented in the paper and written the manuscript. Rana Hashmy has done the overall guidance for the project and paper as well. She has provided high level guidance at all the stages of this research project. She has provided the environments for carrying out this research and has supported in providing all the infrastructure for processing the research project. Hilal Ahmad Khanday has played a foundational and supportive role by providing essential structure, assistance and coordination throughout the project. He has been instrumental in optimizing algorithms which is essential for enhancing the efficiency and performance of the research.
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Ganai, A.H., Hashmy, R. & Khanday, H.A. Finding Information Diffusion’s Seed Nodes in Online Social Networks Using a Special Degree Centrality. SN COMPUT. SCI. 5, 333 (2024). https://doi.org/10.1007/s42979-024-02683-x
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DOI: https://doi.org/10.1007/s42979-024-02683-x