CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models (2404.17059v1)
Abstract: In recent years, there has been increasing interest in network diffusion models and related problems. The most popular of these are the independent cascade and linear threshold models. Much of the recent experimental work done on these models requires a large number of simulations conducted on large graphs, a computationally expensive task suited for low-level languages. However, many researchers prefer the use of higher-level languages (such as Python) for their flexibility and shorter development times. Moreover, in many research tasks, these simulations are the most computationally intensive task, so it would be desirable to have a library for these with an interface to a high-level language with the performance of a low-level language. To fill this niche, we introduce CyNetDiff, a Python library with components written in Cython to provide improved performance for these computationally intensive diffusion tasks.
- Stochastic Top K-Subset Bandits with Linear Space and Non-Linear Feedback with Applications to Social Influence Maximization. ACM/IMS Transactions on Data Science (TDS) 2, 4 (2022), 1–39.
- Cython: The Best of Both Worlds. Computing in Science & Engineering 13, 2 (2011), 31–39. https://doi.org/10.1109/MCSE.2010.118
- Rebekka Burkholz and John Quackenbush. 2021. Cascade size distributions: Why they matter and how to compute them efficiently. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 6840–6849.
- Scalable lattice influence maximization. IEEE Trans. Comp. Soc. Sys. 7, 4 (2020), 956–970.
- Pedro Domingos and Matt Richardson. 2001. Mining the network value of customers. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 57–66.
- Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12, 3 (2001), 211–223.
- Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata. Academy of Marketing Science Review 9, 3 (2001), 1–18.
- A data-based approach to social influence maximization. Proceedings of the VLDB Endowment 5, 1 (2011), 73–84.
- Mark Granovetter. 1978. Threshold models of collective behavior. Amer. J. Sociology 83, 6 (1978), 1420–1443.
- Exploring Network Structure, Dynamics, and Function using NetworkX. In Proceedings of the 7th Python in Science Conference, Gaël Varoquaux, Travis Vaught, and Jarrod Millman (Eds.). Pasadena, CA USA, 11 – 15.
- Terence Kelly. 2020. Programming Workbench: Compressed Sparse Row Format for Representing Graphs. login Usenix Mag. 45, 4 (2020). https://www.usenix.org/publications/login/winter2020/kelly
- Maximizing the spread of influence through a social network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 137–146.
- Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 420–429.
- Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.
- An analysis of approximations for maximizing submodular set functions—I. Mathematical Programming 14, 1 (1978), 265–294.
- An Explore-then-Commit Algorithm for Submodular Maximization Under Full-bandit Feedback. In The 38th Conference on Uncertainty in Artificial Intelligence.
- NDlib: a python library to model and analyze diffusion processes over complex networks. Int. J. Data Sci. Anal. 5, 1 (2018), 61–79. https://doi.org/10.1007/S41060-017-0086-6
- Thomas C Schelling. 2006. Micromotives and Macrobehavior. WW Norton & Company.
- Abhishek K Umrawal and Vaneet Aggarwal. 2023. Leveraging the community structure of a social network for maximizing the spread of influence. ACM SIGMETRICS Performance Evaluation Review 50, 4 (2023), 17–19.
- Fractional Budget Allocation for Influence Maximization. In 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 4327–4332.
- A community-aware framework for social influence maximization. IEEE Transactions on Emerging Topics in Computational Intelligence (2023).