Network structure inference, a survey: Motivations, methods, and applications

I Brugere, B Gallagher, TY Berger-Wolf - ACM Computing Surveys …, 2018 - dl.acm.org
Networks represent relationships between entities in many complex systems, spanning from
online social interactions to biological cell development and brain connectivity. In many …

Game-theoretic frameworks for epidemic spreading and human decision-making: A review

Y Huang, Q Zhu - Dynamic Games and Applications, 2022 - Springer
This review presents and reviews various solved and open problems in developing,
analyzing, and mitigating epidemic spreading processes under human decision-making. We …

Influence maximization on social graphs: A survey

Y Li, J Fan, Y Wang, KL Tan - IEEE Transactions on Knowledge …, 2018 - ieeexplore.ieee.org
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 …

Structure and dynamics of information pathways in online media

M Gomez Rodriguez, J Leskovec… - Proceedings of the sixth …, 2013 - dl.acm.org
Diffusion of information, spread of rumors and infectious diseases are all instances of
stochastic processes that occur over the edges of an underlying network. Many times …

Scalable influence estimation in continuous-time diffusion networks

N Du, L Song… - Advances in neural …, 2013 - proceedings.neurips.cc
If a piece of information is released from a media site, can it spread, in 1 month, to a million
web pages? This influence estimation problem is very challenging since both the time …

Robust influence maximization

W Chen, T Lin, Z Tan, M Zhao, X Zhou - Proceedings of the 22nd ACM …, 2016 - dl.acm.org
In this paper, we address the important issue of uncertainty in the edge influence probability
estimates for the well studied influence maximization problem---the task of finding k seed …

Network reconstruction and community detection from dynamics

TP Peixoto - Physical review letters, 2019 - APS
We present a scalable nonparametric Bayesian method to perform network reconstruction
from observed functional behavior that at the same time infers the communities present in …

Influence estimation and maximization in continuous-time diffusion networks

M Gomez-Rodriguez, L Song, N Du, H Zha… - ACM Transactions on …, 2016 - dl.acm.org
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 …

Using humans as sensors: an estimation-theoretic perspective

D Wang, MT Amin, S Li, T Abdelzaher… - … -14 proceedings of …, 2014 - ieeexplore.ieee.org
The explosive growth in social network content suggests that the largest “sensor network”
yet might be human. Extending the participatory sensing model, this paper explores the …

Representation learning for information diffusion through social networks: an embedded cascade model

S Bourigault, S Lamprier, P Gallinari - … on Web Search and Data Mining, 2016 - dl.acm.org
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 …