Influence maximization in social networks using graph embedding and graph neural network
With the boom in technologies and mobile networks in recent years, online social networks
have become an integral part of our daily lives. These virtual networks connect people …
have become an integral part of our daily lives. These virtual networks connect people …
Influence maximization in social networks using transfer learning via graph-based LSTM
Social networks have emerged as efficient platforms to connect people worldwide and
facilitate the rapid spread of information. Identifying influential nodes in social networks to …
facilitate the rapid spread of information. Identifying influential nodes in social networks to …
Deep Graph Representation Learning and Optimization for Influence Maximization
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
Influence maximization in complex networks by using evolutionary deep reinforcement learning
Influence maximization (IM) in complex networks tries to activate a small subset of seed
nodes that could maximize the propagation of influence. The studies on IM have attracted …
nodes that could maximize the propagation of influence. The studies on IM have attracted …
Determination of influential nodes based on the Communities' structure to maximize influence in social networks
With the increasing development of social networks, they have turned into important
research platforms. Influence maximization is one of the most important research issues in …
research platforms. Influence maximization is one of the most important research issues in …
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 …
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 k …
research attention in the literature. The aim of IM, which is NP-hard, is to select a set of k …
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 …
Lense: Learning to navigate subgraph embeddings for large-scale combinatorial optimisation
Combinatorial Optimisation problems arise in several application domains and are often
formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are …
formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are …
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 …