@inproceedings{TasninaCrovellaMurali:PSB26,
  author	= {Tasnina, Nure and Crovella, Mark and Murali, T. M.},
  title		= {Provenance Tracing in Network Diffusion Algorithms},
  booktitle	= {Proceedings of the Pacific Symposium on Biocomputing (PSB)},
  year		= {2026},
  month		= jan,
  URL		= {TDB},
  abstract	= {We propose a novel strategy for provenance tracing in network diffusion algorithms, a problem that has been surprisingly overlooked in spite of the widespread use of diffusion algorithms in biological applications. Our path-based approach enables ranking paths by the magnitude of their contribution to each node's score, offering fine-grained insight into how information propagates through a network. Building on this capability, we introduce two quantitative measures: (i) path-based effective diffusion, which evaluates how well a diffusion algorithm leverages the full topology of a network, and (ii) diffusion betweenness, which quantifies a node's importance in propagating scores. We applied our framework to SARS-CoV-2 protein interactors and human PPI networks. Provenance tracing of the Regularized Laplacian and Random walk with restart revealed that a substantial amount of a node's score is contributed via multi-edge paths, demonstrating that diffusion algorithms exploit the global structure of the network. Analysis of diffusion betweenness identified proteins playing a critical role in score propagation; proteins with high diffusion betweenness were enriched with essential human genes and interactors of other viruses, supporting the biological interpretability of the metric. Finally, in a signaling network composed of causal interactions between human proteins, the top contributing paths showed strong overlap with COVID-19-related pathways. These results suggest that our path-based framework offers valuable insight into diffusion algorithms and can serve as a powerful tool for interpreting diffusion scores in a biologically meaningful context, complementing existing module- or node-centric approaches in systems biology.},
  doi		= {TBD}
}