Learn More
Real-world networks, such as the World Wide Web and online social networks, are <i>very large</i> and are <i>evolving rapidly</i>. Thus tracking personalized PageRank in such evolving networks is an important challenge in network analysis and graph mining. In this paper, we propose an efficient online algorithm for tracking personalized PageRank in an(More)
Influence maximization is a problem to find small sets of highly influential individuals in a social network to maximize the spread of influence under stochastic cascade models of propagation. Although the problem has been well-studied, it is still highly challenging to find solutions of high quality in large-scale networks of the day. While(More)
A k-submodular function is a generalization of a submodular function, where the input consists of k disjoint subsets, instead of a single subset, of the domain. Many machine learning problems, including influence maximization with k kinds of topics and sensor placement with k kinds of sensors, can be naturally modeled as the problem of maximizing monotone(More)
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,(More)
  • 1