[HTML][HTML] MAHE-IM: Multiple aggregation of heterogeneous relation embedding for influence maximization on heterogeneous information network
Y Li, L Li, Y Liu, Q Li - Expert Systems with Applications, 2022 - Elsevier
Influence maximization (IM), as an essential problem in social network analysis, can identify
a minimum group of the influential nodes to maximize the spread of information on the …
a minimum group of the influential nodes to maximize the spread of information on the …
Leveraging transfer learning in reinforcement learning to tackle competitive influence maximization
Competitive influence maximization (CIM) is a key problem that seeks highly influential
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
CIM: clique-based heuristic for finding influential nodes in multilayer networks
M Katukuri, M Jagarapu - Applied Intelligence, 2022 - Springer
Abstract Identifying Influential nodes (Influence maximization) in complex networks is an
essential factor for spreading and controlling the information spreading dynamics in social …
essential factor for spreading and controlling the information spreading dynamics in social …
Socialbots on fire: Modeling adversarial behaviors of socialbots via multi-agent hierarchical reinforcement learning
Socialbots are software-driven user accounts on social platforms, acting autonomously
(mimicking human behavior), with the aims to influence the opinions of other users or spread …
(mimicking human behavior), with the aims to influence the opinions of other users or spread …
Social-inverse: Inverse decision-making of social contagion management with task migrations
G Tong - Advances in Neural Information Processing …, 2022 - proceedings.neurips.cc
Considering two decision-making tasks $ A $ and $ B $, each of which wishes to compute
an effective decision $ Y $ for a given query $ X $, can we solve task $ B $ by using query …
an effective decision $ Y $ for a given query $ X $, can we solve task $ B $ by using query …
Math‐based reinforcement learning for the adaptive budgeted influence maximization problem
In social networks, the influence maximization problem requires selecting an initial set of
nodes to influence so that the spread of influence can reach its maximum under certain …
nodes to influence so that the spread of influence can reach its maximum under certain …
[HTML][HTML] Influence maximization diffusion models based on engagement and activeness on instagram
An influencer is an impactful content creator on social media. The emergence of influencers
led to increased influencer marketing. The task of picking the right influencers is widely …
led to increased influencer marketing. The task of picking the right influencers is widely …
Hypernetwork dismantling via deep reinforcement learning
Network dismantling aims to degrade the connectivity of a network by removing an optimal
set of nodes. It has been widely adopted in many real-world applications such as epidemic …
set of nodes. It has been widely adopted in many real-world applications such as epidemic …
[PDF][PDF] Complex contagion influence maximization: a reinforcement learning approach
In influence maximization (IM), the goal is to find a set of seed nodes in a social network that
maximizes the influence spread. While most IM problems focus on classical influence …
maximizes the influence spread. While most IM problems focus on classical influence …
NEDRL-CIM: Network embedding meets deep reinforcement learning to tackle competitive influence maximization on evolving social networks
Competitive Influence Maximization (CIM) aims to maximize the influence of a party given
the competition from other parties in the same social network, like companies find key users …
the competition from other parties in the same social network, like companies find key users …