Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes

By Ke Zhou, Hongyuan Zha, Le Song. 2013

430 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, background): “A sequence of recent work has argued that modeling cascade data and information diffusion using continuous-time diffusion networks can provide significantly more accurate models than discretetime models [8, 9, 10, 11, 12, 13, 14, 15].”
Granger Causality: A Review and Recent Advances (2021, background): “Recent work gained traction by focusing speciﬁcally on Hawkes processes, describing self-and mutually-excitatory processes (Zhou et al., 2013; Eichler et al., 2017; Xu et al., 2016).”
Rough fractional diffusions as scaling limits of nearly unstable heavy tailed Hawkes processes (2015, methodology): “In the last years, the probabilistic and statistical analysis of Hawkes processes has known several interesting developments, driven by the recent use of Hawkes processes in various applied fields such as neurosciences [13, 33, 34, 36], sociology [9, 28, 41], criminology [30, 31], genome analysis [3”
Combating Fake News (2019, methodology): “There are several well known models used to represent information diffusion in social networks, such as the Independent Cascade (IC) and Linear Threshold (LT) model [47] and point process models such as Hawkes Process model [129].”
Transformer Hawkes Process (2020, methodology): “We can use graphs to model these follower-followee relationships (Zhou et al., 2013; Farajtabar et al., 2017), where each vertex corresponds to a specific user and each edge represents connections between the two associated users.”
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, background): “, [22, 23, 33, 34, 36]) is that we assume the process intensity λt depends on another stochastic process pt, the post infectiousness.”
Time-Sensitive Recommendation From Recurrent User Activities (2015, methodology): “However, the algorithm in [29] requires, at each iteration, a full SVD for computing the proximal operator, which is often prohibitive with large matrices.”
Graph Structure Learning for Robust Graph Neural Networks (2020, background): “Many real-world graphs are naturally low-rank and sparse as the entities usually tend to form communities and would only be connected with a small number of neighbors [37].”