Influence Maximization (IM) has attracted significant attention due to its widespread applications, such as viral marketing and rumor control. In realworld social networks, users have their own interests (which can be represented as topics) and are more likely to be influenced by their friends (or friends' friends) with similar topics. Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting k number of optimal seed nodes, given that influencing these nodes can be increased by taking Topics into consideration. The phenomenon of influence propagation is concerned with how influence spreads in a network from a set of seeds. Independent Cascade Model (ICM) is a stochastic information diffusion model where the information flows over the network through Cascade. Using this model in the current thesis, a Topicaware robust approach is taken to find the most influential nodes. The proposed robust model maximizes the number of infected nodes while considering the presence of the competitor's nodes and their attempt to maximize their own influence as well in the same social network. Experiments are performed on a real textmessaging social network. The model developed here significantly outperforms some of the wellknown existing algorithms like Random Selection, High Degree, and CELF Greedy.
Influence Maximization (IM) has attracted significant attention due to its widespread applications, such as viral marketing and rumor control. In realworld social networks, users have their own interests (which can be represented as topics) and are more likely to be influenced by their friends (or friends' friends) with similar topics. Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting k number of optimal seed nodes, given that influencing these nodes can be increased by taking Topics into consideration. The phenomenon of influence propagation is concerned with how influence spreads in a network from a set of seeds. Independent Cascade Model (ICM) is a stochastic information diffusion model where the information flows over the network through Cascade. Using this model in the current thesis, a Topicaware robust approach is taken to find the most influential nodes. The proposed robust model maximizes the number of infected nodes while considering the presence of the competitor's nodes and their attempt to maximize their own influence as well in the same social network. Experiments are performed on a real textmessaging social network. The model developed here significantly outperforms some of the wellknown existing algorithms like Random Selection, High Degree, and CELF Greedy.
Topicbased Influence Maximization in Presence of Competitors
MODARESI, SEYEDMOHAMADJAVAD
2022/2023
Abstract
Influence Maximization (IM) has attracted significant attention due to its widespread applications, such as viral marketing and rumor control. In realworld social networks, users have their own interests (which can be represented as topics) and are more likely to be influenced by their friends (or friends' friends) with similar topics. Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting k number of optimal seed nodes, given that influencing these nodes can be increased by taking Topics into consideration. The phenomenon of influence propagation is concerned with how influence spreads in a network from a set of seeds. Independent Cascade Model (ICM) is a stochastic information diffusion model where the information flows over the network through Cascade. Using this model in the current thesis, a Topicaware robust approach is taken to find the most influential nodes. The proposed robust model maximizes the number of infected nodes while considering the presence of the competitor's nodes and their attempt to maximize their own influence as well in the same social network. Experiments are performed on a real textmessaging social network. The model developed here significantly outperforms some of the wellknown existing algorithms like Random Selection, High Degree, and CELF Greedy.File  Dimensione  Formato  

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https://hdl.handle.net/20.500.12608/46945