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Recognition of the top k users (seed) in social networks is a challenge in influence maximization (IM), which aims to increase influence propagation. Finding the network's most important nodes can help network analysts track the spread of rumors, diseases, and data in many applications. We provide the Influence Maximization in Social Networks Based on Community Structure (IMSC) technique to address this problem. The three phases of our suggested framework, IMSC, are as follows: the network's community structure is identified, candidates are generated using community information, and seed nodes are finally selected from the candidate set. In this phase, the IMSC method looks at a network or group of interconnected things (like social connections or data points). It determines how they naturally form smaller communities or groups. After identifying these groups, the method generates a list of potential candidates or members who could play a key role in spreading data or influence within each community. Finally, from the list of potential candidates, the method selects a few critical individuals called "seed nodes." These seed nodes are starting points for spreading information or influence throughout the network. Our tests on real-world datasets display that the proposed method surpasses the competition in terms of the value of the corresponding output influential nodes while still using an acceptable amount of memory and processing time for massive graphs. We assess our algorithm against state-of-the-art solutions to the influence maximization issue. [ABSTRACT FROM AUTHOR]

The popularity of social networks is growing and offering promising opportunities for practical applications, such as computer networks, social systems, physical systems, and biological networks. The use of networks provides valuable insights into the patterns, relationships, and behavior of various entities and their interactions. Healthcare service systems are critical social systems that can be analyzed using network-based methodologies, one of which is influence maximization (IM). It is a network analysis technique that identifies a small subset of suppliers in a network that has the most influence on the overall behavior of the healthcare system. By utilizing network analysis techniques, data scientists can identify the most crucial healthcare providers in a network and better understand the system's operation. The paper uses a network global structure-based centrality (ngsc) approach that combines conventional k-shell and the sum of neighbors' degree methods with information about the network's global structural features to identify significant providers in the provider-interacting network. The experimental outcomes on five real-world networks reveal that the proposed NGSC technique outperforms earlier benchmark methods in identifying significant providers under a spreading dynamic when using the IC model. [ABSTRACT FROM AUTHOR]