A complex network-based vaccination strategy for infectious diseases
In the event of an outbreak, it is vital to stop the spread of infectious diseases as quickly as
possible, and vaccination is the most important means of stopping the spread of infectious …
possible, and vaccination is the most important means of stopping the spread of infectious …
ARIS: Efficient admitted influence maximizing in large-scale networks based on valid path reverse influence sampling
Influence maximization problem has been extensively studied in recent years. It aims at
finding a seed set consisting of vertices from a network, so that their collective influence …
finding a seed set consisting of vertices from a network, so that their collective influence …
Non-uniform influence blocking maximization in social network
Abstract Online Social Network (OSN) is one of the most popular internet services. It also
has become the main source of news for many people. Despite all the benefits, OSN …
has become the main source of news for many people. Despite all the benefits, OSN …
[HTML][HTML] GCNT: Identify influential seed set effectively in social networks by integrating graph convolutional networks with graph transformers
J Tang, J Qu, S Song, Z Zhao, Q Du - Journal of King Saud University …, 2024 - Elsevier
Exploring effective and efficient strategies for identifying influential nodes from social
networks as seeds to promote the propagation of influence remains a crucial challenge in …
networks as seeds to promote the propagation of influence remains a crucial challenge in …
FuseIM: Fusing Probabilistic Traversals for Influence Maximization on Exascale Systems
Probabilistic breadth-first traversals (BPTs) are used in many network science and graph
machine learning applications. In this paper, we are motivated by the application of BPTs in …
machine learning applications. In this paper, we are motivated by the application of BPTs in …
Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge
K You, S Liu, Y Bai - Complex & Intelligent Systems, 2025 - Springer
Influence Maximization (IM) stands as a central challenge within the domain of complex
network analysis, with the primary objective of identifying an optimal seed set of a …
network analysis, with the primary objective of identifying an optimal seed set of a …
RIMR: Reverse Influence Maximization Rank
The ubiquitous usage of social media and other internet platforms has led to the widespread
proliferation of naturally occurring social networks. This has caused a revolution in …
proliferation of naturally occurring social networks. This has caused a revolution in …
DiFuseR: a distributed sketch-based influence maximization algorithm for GPUs
Influence maximization (IM) aims to find a given number of “seed" vertices that can
effectively maximize the expected spread under a given diffusion model. Due to the NP …
effectively maximize the expected spread under a given diffusion model. Due to the NP …
BatchedGreedy: A batch processing approach for influence maximization with candidate constraint
X Han, X Yao, H Huang - Applied Intelligence, 2023 - Springer
Influence maximization (IM) aims to find k seed nodes from social network G to maximize the
spread of influence under a given diffusion model. However, in real social marketing …
spread of influence under a given diffusion model. However, in real social marketing …
Fused Breadth-First Probabilistic Traversals on Distributed GPU Systems
Probabilistic breadth-first traversals (BPTs) are used in many network science and graph
machine learning applications. In this paper, we are motivated by the application of BPTs in …
machine learning applications. In this paper, we are motivated by the application of BPTs in …