Information propagation in microblog networks

Zhang, Chenyi; Sun, Jianling; Wang, Ke

doi:10.1145/2492517.2492608

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…It first learns parameters based on user's previous consumption information and adds time dynamics to it. Zhang et al [35] proposed a topic-sensitive solution that can analyze influence in microblog networks. Li et al [36] proposed a keywordbased target influence maximization problem (KB-TIM), which aims to select a set of seed nodes to maximize the impact among users associated with a given advertisement.…”

Section: Topic-sensitive Influence Maximizationmentioning

confidence: 99%

Human-Driven Dynamic Community Influence Maximization in Social Media Data Streams

Shi

et al. 2020

IEEE Access

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Microblogging-a popular social media service platform-has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for maximizing community influence which has broad application prospects in recommendation system, advertising and other fields. With the rapid development of the mobile Internet, online social networks are gradually infiltrating into our daily lives, in which the communities are an important part of social networks. The combination of social networking and edge computing technology has important application value and is the development trend of influence maximization in future networks. However, traditional influence maximization models look for the most influential seed nodes while ignoring the fact that the selected seed nodes are various for different event topics, which significantly reduces the efficiency and accuracy of event propagation. In addition, most existing methods focus only on event propagation and neglect multiple topics in event propagation. At the same time, the interests of users in the network are not always single and the user's interest and the topic of the event will change over time, thus making it challenging to track momentous events in a timely manner. To address these issues, this paper proposes a Multi-Topic Learning-based Independent Cascade model (MTL-IC), and a Similarity Priority Mechanism-based Event Evolution model (SPM-EE). MTL-IC incorporates multi-topic factors and considers the authority and hub in interests of user, which makes the results more efficient and more accurate. SPM-EE can update the seed users according to their changeable interest in time, which largely improve the precision of event evolution. The experimental results, using Twitter datasets, demonstrate the effectiveness of our proposed methods for both dynamic community influence maximization and event evolution.

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Section: Topic-sensitive Influence Maximizationmentioning

confidence: 99%

Human-Driven Dynamic Community Influence Maximization in Social Media Data Streams

Shi

et al. 2020

IEEE Access

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“…We also derive an upper bound on the influence spread to further speed up our hop-based algorithms. Our hop-based approaches can be easily applied to many influence-based applications, such as topicaware influence maximization [30] and community detection via influence maximization [14].…”

Section: Related Workmentioning

confidence: 99%

Influence Maximization Meets Efficiency and Effectiveness

Tang

Yuan

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hopbased algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hopbased approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.Index Terms-Online social networks, influence maximization, hop-based influence estimation.

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“…Topic-aware models [6,15,113] consider each edge u, v to be associated with a propagation probability p z u,v on each topic z. A node u can influence each of its neighbors v via a mixed probability dependent on the topics involved in the queries.…”

Section: Other Diffusion Modelsmentioning

confidence: 99%

Seed selection for viral marketing in online social networks : from influence maximization to profit maximization

Tang¹

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Online Social Networks (OSNs) attract billions of users. Information can be disseminated widely and rapidly through OSNs with "word-of-mouth" effects. Viral marketing is such a typical application in which new products or commercial activities are advertised by some seed users in OSNs to other users in a cascading manner. A large amount of recent work has been focusing on viral marketing in OSNs. In this thesis, three seed selection problems in viral marketing are investigated. We start from the classical influence maximization. Then, we look at profit maximization for advertisers and OSN providers respectively that naturally combines the benefit with the cost of viral marketing. Recent research has adopted the sampling approach to achieve a (1 − 1/e − ε)-I would take this opportunity to express my deepest gratitude to my advisors in Nanyang Technological University (NTU). They are my supervisor Prof. Tang Xueyan, cosupervisor Prof. Yuan Junsong, and mentor Prof. Cai Wentong. They gave me many useful suggestions that inspired me in many aspects of my research direction. Without their guidance, the achievements of my research work will never be possible. I would also like to thank all my fellow and colleagues for the projects we are working together, my relatives and friends for their encouragements and the staffs in Multi-plAtform Game Innovation Centre (MAGIC) for their technical supports. In particular, I appreciate Prof.

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Information propagation in microblog networks

Cited by 13 publications

References 23 publications

Human-Driven Dynamic Community Influence Maximization in Social Media Data Streams

Human-Driven Dynamic Community Influence Maximization in Social Media Data Streams

Influence Maximization Meets Efficiency and Effectiveness

Seed selection for viral marketing in online social networks : from influence maximization to profit maximization

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