3 research outputs found
Influence Maximization with Fairness at Scale (Extended Version)
In this paper, we revisit the problem of influence maximization with
fairness, which aims to select k influential nodes to maximise the spread of
information in a network, while ensuring that selected sensitive user
attributes are fairly affected, i.e., are proportionally similar between the
original network and the affected users. Recent studies on this problem focused
only on extremely small networks, hence the challenge remains on how to achieve
a scalable solution, applicable to networks with millions or billions of nodes.
We propose an approach that is based on learning node representations for fair
spread from diffusion cascades, instead of the social connectivity s.t. we can
deal with very large graphs. We propose two data-driven approaches: (a)
fairness-based participant sampling (FPS), and (b) fairness as context (FAC).
Spread related user features, such as the probability of diffusing information
to others, are derived from the historical information cascades, using a deep
neural network. The extracted features are then used in selecting influencers
that maximize the influence spread, while being also fair with respect to the
chosen sensitive attributes. In FPS, fairness and cascade length information
are considered independently in the decision-making process, while FAC
considers these information facets jointly and considers correlations between
them. The proposed algorithms are generic and represent the first policy-driven
solutions that can be applied to arbitrary sets of sensitive attributes at
scale. We evaluate the performance of our solutions on a real-world public
dataset (Sina Weibo) and on a hybrid real-synthethic dataset (Digg), which
exhibit all the facets that we exploit, namely diffusion network, diffusion
traces, and user profiles. These experiments show that our methods outperform
the state-the-art solutions in terms of spread, fairness, and scalability
Influence Maximization using Influence and Susceptibility Embeddings
International audienceFinding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread