Evolving Influence Maximization in Evolving Networks

Evolving Influence Maximization

Influence Maximization (IM) aims to maximize the number of people that become aware of a product by finding the ‘best’ set of ‘seed’ users to initiate the product advertisement. Unlike prior arts on static social networks containing fixed number of users, we undertake the first study of IM in more realistic evolving networks with temporally growing topology. The task of evolving IM (EIM), howev...

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation

Dynamic Influence Analysis in Evolving Networks

We propose the first real-time fully-dynamic index data structure designed for influence analysis on evolving networks. With this aim, we carefully redesign the data structure of the state-of-the-art sketching method introduced by Borgs et al., and construct corresponding update algorithms. Using this index, we present algorithms for two kinds of queries, influence estimation and influence maxi...

On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the change...

Evolving networks

Most real networks often evolve through time: changes of topology can occur if some nodes and/or edges appear and/or disappear, and the types or weights of nodes and edges can also change even if the topology stays static. Mobile devices with wireless capabilities (mobile phones, laptops, etc.) are a typical example of evolving networks where nodes or users are spread in the environment and con...