Shaping Social Activity by Incentivizing Users MPI for Software Systems †
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.
Quality:The paper is technically sound, well-written, and addresses a solid problem which one could connect to practical applications. Activity shaping is at least as hard as influence maximization.The main weakness of the paper are the experimental results. The results must necessarily be done via simulation, because activity shaping is an interventional problem, and they do not have a social network into which they could intervene. The metric for comparing heuristics is not all that compelling, but the authors take a lot of care to show their approach competes against reasonable baselines, on simulated data.Clarity:The paper is well written, and accessible to a reader who is not familiar with Hawkes processes. Section 2 provides a good, self-contained, explanation of the necessary background material on point processes and branching processes. Significance:The foundational assumption appears to be that the exogenous events (shaping) happens only at the beginning of the process. All subsequent evolution in the point process is endogenous. The paper’s assumptions diverge from reality if, for example,1. The shaping incentives are doled out throughout the evolution of the process.2. There are multiple competing or interacting incentives (though, I believe the Hawkes process construction could handle this case).The authors do get something for making this assumption: "a linear relation between the expected intensity of the network and the intensity of the exogenous events" (L192), which turns activity shaping into a convex optimization.I’d welcome some commentary on whether the Hawkes process construction would work if incentives were doled out throughout the process, e.g., in a sequential decision making setting.Originality:As the authors point out, Hawkes processes have had some prominence in the ML literature in the last couple of years. This paper is a solid extension that line of thinking: i.e., taking a self-exciting process, and applying it to a social network which exhibits self-exciting behavior (cascades). Solid incremental work, which describes most papers accepted at NIPS.Once the problem is framed as a convex optimization in Section 4, the optimization itself is a straightforward projected gradient method (Algorithm 1). The main significance of the paper is the formulation of activity shaping as a convex optimization, using a Hawkes process.Questions:L262: Is the matrix exponential in Section 5 a consequence of the specific kernel chosen for this paper, or is it required for any kernel G ?Misc:Bibliography: Check your Bibtex (e.g., “hawkes” vs. “Hawkes”)