Reinforcement learning on graphs: A survey
M Nie, D Chen, D Wang - IEEE Transactions on Emerging …, 2023 - ieeexplore.ieee.org
Graph mining tasks arise from many different application domains, including social
networks, biological networks, transportation, and E-commerce, which have been receiving …
networks, biological networks, transportation, and E-commerce, which have been receiving …
A multi-transformation evolutionary framework for influence maximization in social networks
Influence maximization is a crucial issue for mining the deep information of social networks,
which aims to select a seed set from the network to maximize the number of influenced …
which aims to select a seed set from the network to maximize the number of influenced …
On Implementing Social Community Clouds Based on Markov Models
S Souravlas, SD Anastasiadou - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Social networks reflect, to a wide extent, the real-world relationships that allow users to
connect and share information. The number of people that interact in social networks keeps …
connect and share information. The number of people that interact in social networks keeps …
A Memetic algorithm for determining robust and influential seeds against structural perturbances in competitive networks
S Wang, X Tan - Information Sciences, 2023 - Elsevier
The influence maximization problem has attracted increasing attention in previous studies.
Recent years have witnessed an enormous interest in the modeling, performance …
Recent years have witnessed an enormous interest in the modeling, performance …
Influence maximization in social networks with privacy protection
XJ Zhang, J Wang, XJ Ma, C Ma, JQ Kan… - Physica A: Statistical …, 2022 - Elsevier
With the explosive development of online social network platforms, how to find a small
subset of users (seed nodes) across multiple social networks to maximize the spread of …
subset of users (seed nodes) across multiple social networks to maximize the spread of …
A Node Classification-Based Multiobjective Evolutionary Algorithm for Community Detection in Complex Networks
Multiobjective evolutionary algorithms (MOEAs) have been widely used in community
detection in recent years. However, most of the existing MOEA-based ones adopted the …
detection in recent years. However, most of the existing MOEA-based ones adopted the …
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …
widely used in mobile networks, social computing, and recommendation systems. It aims at …
Identification of Key Actor Nodes: A Centrality Measure Ranking Aggregation Approach
A Kosmatopoulos, K Loumponias… - 2022 IEEE/ACM …, 2022 - ieeexplore.ieee.org
The identification of key actors in complex networks has gathered significant interest by
virtue of their importance in modern applications. Several of the existing methods employ …
virtue of their importance in modern applications. Several of the existing methods employ …
Large-scale multi-objective influence maximisation with network downscaling
Finding the most influential nodes in a network is a computationally hard problem with
several possible applications in various kinds of network-based problems. While several …
several possible applications in various kinds of network-based problems. While several …
[PDF][PDF] Large-scale multi-objective influence maximisation with network downscaling
D Bucur - arXiv preprint arXiv:2204.06250, 2022 - academia.edu
Finding the most influential nodes in a network is a computationally hard problem with
several possible applications in various kinds of network-based problems. While several …
several possible applications in various kinds of network-based problems. While several …