While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

Influence Maximization (IM), which selects a set of k users (called seed set) from a social
network to maximize the expected number of influenced users (called influence spread), is a …

Graph representation learning serves as the core of important prediction tasks, ranging from
product recommendation to fraud detection. Real-life graphs usually have complex …

Diffusion is a fundamental graph process, underpinning such phenomena as epidemic
disease contagion and the spread of innovation by word-of-mouth. We address the …

The study of influence-driven propagations in social networks and its exploitation for viral
marketing purposes has recently received a large deal of attention. However, regardless of …

Influence maximization is a classic optimization problem studied in the area of social
network analysis and viral marketing. Given a network, it is defined as the problem of finding …

If a piece of information is released from a set of media sites, can it spread, in 1 month, to a
million web pages? Can we efficiently find a small set of media sites among millions that can …

In this paper, we focus on information diffusion through social networks. Based on the well-
known Independent Cascade model, we embed users of the social network in a latent space …

Influence maximization, defined as a problem of finding a set of seed nodes to trigger a
maximized spread of influence, is crucial to viral marketing on social networks. For practical …

Memory replay, which stores a subset of historical data from previous tasks to replay while
learning new tasks, exhibits state-of-the-art performance for various continual learning …