Real-time topic-aware influence maximization using preprocessing
نویسندگان
چکیده
منابع مشابه
Real-time topic-aware influence maximization using preprocessing
Background Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc...
متن کاملOnline Topic-Aware Influence Maximization
Influence maximization, whose objective is to select k users (called seeds) from a social network such that the number of users influenced by the seeds (called influence spread) is maximized, has attracted significant attention due to its widespread applications, such as viral marketing and rumor control. However, in real-world social networks, users have their own interests (which can be repre...
متن کاملOnline Topic-aware Influence Maximization Queries
Influence maximization is the key algorithmic problem behind viral marketing: it requires to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network, leading to a large number of adoptions. Although real world users evidently have di↵erent degrees of interest and authoritativeness on di↵erent topics, the bulk of...
متن کاملTopic-aware Social Influence Minimization
In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking k nodes out...
متن کاملCommunity Aware Influence Maximization on Large Scale Networks Using Mapreduce
Influence maximization problem is a well known problem to find the top-k seed users who can maximize the spread of information in a social network. The primary concern is monte carlo simulations method is suffering with scalability issues while the selection of seed users .It takes days to find potential seed users in large datasets. In this paper, we propose a highly scalable algorithm for ide...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Social Networks
سال: 2016
ISSN: 2197-4314
DOI: 10.1186/s40649-016-0033-z