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Abstract
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Effective nodes in social networks are people who have a greater ability to influence others due to their position or extensive connections. These nodes usually have characteristics such as a high number of connections (high degree of edges) or a central position in the network. For example, in social media, these nodes can be famous people who have a large number of followers, and as soon as a message is shared, that message spreads quickly in the network. For this reason, effective nodes can play a key role in disseminating information, creating awareness, and even shaping public attitudes and opinions .
In general, the role of effective nodes in social networks is very important and is considered in various fields such as politics, marketing and even promoting social and cultural awareness. Smart use of these nodes can have significant results in promoting messages, improving public awareness, and even changing social behaviors. The existence of various applications that are effective in identifying users has caused many different approaches to determine these users. These approaches try to discover effective users in a social network composed of nodes and edges. However, as acknowledged in , the problem of finding effective users is an NP-hard problem and cannot be solved with polynomial degree. This has made this research topic as an attractive area for academic researchers, digital marketing, government as well as industry.
The structural features of nodes in a social network refer to the characteristics and features that are obtained from the structure and connections of the network and indicate the position, role, or importance of a node in the network. These features are usually extracted using a graph that models the network and can be used to analyze the social network, predict the behavior of nodes, or understand network dynamics [8].
Deep learning has wide applications in social networks and helps to analyze and process a huge amount of data. One of the
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