arXiv:2606.07483v1 Announce Type: new Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion. A central challenge is to recover the hidden influence network behind these cascades. Existing methods typically assume a specific diffusion model, and their performance degrades substantially when that assumption is misspecified. We propose CascadeNet, a Jacobian-based machine learning framework for network recovery that does not require specifying a diffusion mechanism. The key idea is that the underlyin
The proliferation of complex dynamic systems and large-scale data necessitates more robust and adaptable methods for understanding underlying network structures, moving beyond model-specific assumptions.
Improving network recovery in complex systems has broad implications for understanding and influencing dynamics in areas from public health and finance to social media and product diffusion, enabling more effective interventions.
The development of a diffusion-mechanism-agnostic network recovery framework changes how researchers and practitioners can analyze complex cascade data, potentially leading to more accurate insights and predictions across various domains.
- · Machine Learning Researchers
- · Epidemiologists
- · Social Network Analysts
- · Strategic Planners
- · Model-specific Network Recovery Techniques
- · Organizations relying on inaccurate cascade predictions
More accurate identification of critical nodes and pathways in various cascade phenomena will become possible.
This improved understanding could lead to optimization of interventions in areas like disease control, marketing strategies, or information security.
Societies could become more resilient to negative cascades and more effective at propagating positive ones, fundamentally altering dynamics across many sectors.
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Read at arXiv cs.LG