A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Social network analysis using deep learning: applications and schemes
AM Abbas - Social Network Analysis and Mining, 2021 - Springer
Online social networks (OSNs) are part of daily life of human beings. Millions of users are
connected through online social networks. Due to very large number of users and huge …
connected through online social networks. Due to very large number of users and huge …
Network dynamic GCN influence maximization algorithm with leader fake labeling mechanism
C Zhang, W Li, D Wei, Y Liu, Z Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Influence maximization is an important technique for its significant value on various social
network applications, such as viral marketing, advertisement, and recommendation …
network applications, such as viral marketing, advertisement, and recommendation …
A fast module identification and filtering approach for influence maximization problem in social networks
In this paper, we explore influence maximization, one of the most widely studied problems in
social network analysis. However, developing an effective algorithm for influence …
social network analysis. However, developing an effective algorithm for influence …
Information transmission mode and IoT community reconstruction based on user influence in opportunistic s ocial networks
J Wu, J Xia, F Gou - Peer-to-Peer Networking and Applications, 2022 - Springer
With the wide popularization of the 5G network and the technology of IoT, a variety of mobile
terminals has become a necessity for people's daily life. In the popular environment of the …
terminals has become a necessity for people's daily life. In the popular environment of the …
Crosswalk: Fairness-enhanced node representation learning
The potential for machine learning systems to amplify social inequities and unfairness is
receiving increasing popular and academic attention. Much recent work has focused on …
receiving increasing popular and academic attention. Much recent work has focused on …
Identification of spreading influence nodes via multi-level structural attributes based on the graph convolutional network
Y Ou, Q Guo, JL Xing, JG Liu - Expert Systems with Applications, 2022 - Elsevier
The network structural properties at the micro-level, community-level and macro-level have
different contributions to the spreading influence of nodes. The challenge is how to make …
different contributions to the spreading influence of nodes. The challenge is how to make …
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 …
[HTML][HTML] Influence maximization algorithm based on Gaussian propagation model
WM Li, Z Li, AM Luvembe, C Yang - Information Sciences, 2021 - Elsevier
The influence of each entity in a network is a crucial index of the network information
dissemination. Greedy influence maximization algorithms suffer from time efficiency and …
dissemination. Greedy influence maximization algorithms suffer from time efficiency and …
Adversarial graph embeddings for fair influence maximization over social networks
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …
reach the maximum possible number of nodes, while only targeting a small initial set of …