Fast algorithms to improve fair information access in networks

2024-09-04

arXiv.org

AI Summary

This paper addresses fairness in information diffusion by selecting $k$ seed nodes under the Independent Cascade model to maximize the minimum activation probability among the most vulnerable nodes. To overcome the reliance on edge-probability estimation and scalability limitations of conventional methods, we design ten efficient, probability-agnostic heuristic algorithms. We introduce three key innovations: (i) an adaptive transmission-parameter selection criterion, (ii) a normalized evaluation metric enabling cross-$k$ comparability, and (iii) a comprehensive benchmark suite comprising 174 real-world networks spanning six domains. Experimental results demonstrate that our meta-learning acceleration framework achieves only a 20% average performance degradation while accelerating computation by 75–130×. It outperforms state-of-the-art methods on 20% of the networks and maintains substantial speed advantages and robustness even on ultra-large-scale graphs.

Technology Category

• Search and Optimization: Heuristic Search
• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

We consider the problem of selecting $k$ seed nodes in a network to maximize the minimum probability of activation under an independent cascade beginning at these seeds. The motivation is to promote fairness by ensuring that even the least advantaged members of the network have good access to information. Our problem can be viewed as a variant of the classic influence maximization objective, but it appears somewhat more difficult to solve: only heuristics are known. Moreover, the scalability of these methods is sharply constrained by the need to repeatedly estimate access probabilities. We design and evaluate a suite of $10$ new scalable algorithms which crucially do not require probability estimation. To facilitate comparison with the state-of-the-art, we make three more contributions which may be of broader interest. We introduce a principled method of selecting a pairwise information transmission parameter used in experimental evaluations, as well as a new performance metric which allows for comparison of algorithms across a range of values for the parameter $k$. Finally, we provide a new benchmark corpus of $174$ networks drawn from $6$ domains. Our algorithms retain most of the performance of the state-of-the-art while reducing running time by orders of magnitude. Specifically, a meta-learner approach is on average only $20%$ less effective than the state-of-the-art on held-out data, but about $75-130$ times faster. Further, the meta-learner's performance exceeds the state-of the-art on about $20%$ of networks, and the magnitude of its running time advantage is maintained on much larger networks.

Problem

• Selecting k seed nodes for fair information access.
• Maximizing minimum activation probability in networks.
• Developing scalable algorithms without probability estimation.

Innovation

• Developed 10 scalable algorithms without probability estimation.
• Introduced a new performance metric for algorithm comparison.
• Created a benchmark corpus of 174 networks from 6 domains.