Contingency-aware influence maximization: A reinforcement learning approach
The influence maximization (IM) problem aims at finding a subset of seed nodes in a social
network that maximize the spread of influence. In this study, we focus on a sub-class of IM …
network that maximize the spread of influence. In this study, we focus on a sub-class of IM …
[PDF][PDF] Please be an Influencer?: Contingency-Aware Influence Maximization.
Most previous work on influence maximization in social networks assumes that the chosen
influencers (or seed nodes) can be influenced with certainty (ie, with no contingencies). In …
influencers (or seed nodes) can be influenced with certainty (ie, with no contingencies). In …
A community-aware framework for social influence maximization
AK Umrawal, CJ Quinn… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
We consider the problem of Influence Maximization (IM), the task of selecting seed nodes in
a social network such that the expected number of nodes influenced is maximized. We …
a social network such that the expected number of nodes influenced is maximized. We …
Maximizing influence in an unknown social network
In many real world applications of influence maximization, practitioners intervene in a
population whose social structure is initially unknown. This poses a multiagent systems …
population whose social structure is initially unknown. This poses a multiagent systems …
Efficient and effective influence maximization in social networks: a hybrid-approach
Influence Maximization (IM) is the problem of finding a seed set composed of k nodes that
maximize their influence spread over a social network. Kempe et al. showed the problem to …
maximize their influence spread over a social network. Kempe et al. showed the problem to …
Piano: Influence maximization meets deep reinforcement learning
H Li, M Xu, SS Bhowmick, JS Rayhan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Since its introduction in 2003, the influence maximization (IM) problem has drawn significant
research attention in the literature. The aim of IM, which is NP-hard, is to select a set of users …
research attention in the literature. The aim of IM, which is NP-hard, is to select a set of users …
Efficient influence maximization under network uncertainty
We consider the influence maximization (IM) problem in a partially visible social network.
The goal is to design a decision-making framework for an autonomous agent to select a …
The goal is to design a decision-making framework for an autonomous agent to select a …
Learning heuristics over large graphs via deep reinforcement learning
There has been an increased interest in discovering heuristics for combinatorial problems
on graphs through machine learning. While existing techniques have primarily focused on …
on graphs through machine learning. While existing techniques have primarily focused on …
IMPC: Influence maximization based on multi-neighbor potential in community networks
The study of influence maximization (IM) has attracted many scholars in recent years due to
its import practical values. Given a social network, it aims at finding a subset of k individuals …
its import practical values. Given a social network, it aims at finding a subset of k individuals …
Capacity constrained influence maximization in social networks
Influence maximization (IM) aims to identify a small number of influential individuals to
maximize the information spread and finds applications in various fields. It was first …
maximize the information spread and finds applications in various fields. It was first …