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 …
Influence maximization in unknown social networks: Learning policies for effective graph sampling
A serious challenge when finding influential actors in real-world social networks is the lack
of knowledge about the structure of the underlying network. Current state-of-the-art methods …
of knowledge about the structure of the underlying network. Current state-of-the-art methods …
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 …
Deep graph representation learning for influence maximization with accelerated inference
Selecting a set of initial users from a social network in order to maximize the envisaged
number of influenced users is known as influence maximization (IM). Researchers have …
number of influenced users is known as influence maximization (IM). Researchers have …
Deep reinforcement learning-based approach to tackle topic-aware influence maximization
S Tian, S Mo, L Wang, Z Peng - Data Science and Engineering, 2020 - Springer
Motivated by the application of viral marketing, the topic-aware influence maximization (TIM)
problem has been proposed to identify the most influential users under given topics. In …
problem has been proposed to identify the most influential users under given topics. In …
Balanced influence maximization in social networks based on deep reinforcement learning
Balanced influence maximization aims to balance the influence maximization of multiple
different entities in social networks and avoid the emergence of filter bubbles and echo …
different entities in social networks and avoid the emergence of filter bubbles and echo …
A unifying framework for fairness-aware influence maximization
The problem of selecting a subset of nodes with greatest influence in a graph, commonly
known as influence maximization, has been well studied over the past decade. This problem …
known as influence maximization, has been well studied over the past decade. This problem …
Disco: Influence maximization meets network embedding and deep learning
Since its introduction in 2003, the influence maximization (IM) problem has drawn significant
research attention in the literature. The aim of IM is to select a set of k users who can …
research attention in the literature. The aim of IM is to select a set of k users who can …
Grain: Improving data efficiency of graph neural networks via diversified influence maximization
Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …
improving the data efficiency of deep learning models on large-scale datasets. However …
Scalable fair influence maximization
Given a graph $ G $, a community structure $\mathcal {C} $, and a budget $ k $, the fair
influence maximization problem aims to select a seed set $ S $($| S|\leq k $) that maximizes …
influence maximization problem aims to select a seed set $ S $($| S|\leq k $) that maximizes …
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