arXiv:2606.12581v1 Announce Type: cross Abstract: Real-world networks are inherently incomplete, noisy, and dynamically evolving, making it difficult to capture all actors and their relationships. Their scale often renders direct analysis computationally demanding. While influence maximisation (IM) has been widely studied, the role of graph reduction as a preprocessing step, and its impact on IM accuracy, remains underexplored. In this work, we introduce the Spreading-Oriented Reduction Benchmark (SORB), an open-source, standardised framework for systematically evaluating IM models across dive
The proliferation of increasingly complex real-world networks necessitates more efficient computational methods for analysis, making graph reduction a timely area of research.
Improving the efficiency and accuracy of influence maximization models through graph reduction directly impacts the efficacy of AI applications in areas like social network analysis, marketing, and public health.
This research introduces a standardized benchmark for evaluating graph reduction techniques, which could lead to better-performing and more reliable influence maximization algorithms.
- · AI researchers
- · Data scientists
- · Social media platforms
- · Marketing analytics firms
- · Inefficient graph analysis methods
Improved performance and scalability of AI systems that rely on network analysis.
More targeted and effective influence campaigns in various domains.
Enhanced ability to model and predict complex system behaviors, from viral marketing to disease spread.
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Read at arXiv cs.AI