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Found 5 papers, 1 papers with code
Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation
Furthermore, we undertake an analysis of the approximation error of FIM for network inference.
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.
IGNiteR: News Recommendation in Microblogging Applications (Extended Version)
Similarly, a time-sensitive user encoder enables us to capture the dynamic preferences of users with an attention-based bidirectional LSTM.
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)
During a campaign, spread seeds are selected sequentially at consecutive rounds, and feedback is collected in the form of the activated nodes at each round.
Bandits Under The Influence (Extended Version)
Our bandit algorithms are tailored precisely to recommendation scenarios where user interests evolve under social influence.
