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1. Quantum causal influence

   https://doi.org/10.1007/JHEP07(2019)042  

   Cotler, Jordan; Han, Xizhi; Qi, Xiao-Liang; Yang, Zhao (July 2019, Journal of High Energy Physics)
2. Adversarial influence maximization

   https://doi.org/10.1109/ISIT.2019.8849828  

   Khim, Justin; Jog, Varun; Loh, Po-Ling (January 2019, 2019 IEEE International Symposium on Information Theory (ISIT))
3. Interpretable Stochastic Block Influence Model: Measuring Social Influence Among Homophilous Communities

   https://doi.org/10.1109/TKDE.2023.3289848  

   Leng, Yan; Sowrirajan, Tara; Zhai, Yujia; Pentland, Alex (January 2023, IEEE Transactions on Knowledge and Data Engineering)

   Decision-making on networks can be explained by both homophily and social influences. While homophily drives the formation of communities with similar characteristics, social influences occur both within and between communities. Social influences can be reasoned through role theory, which indicates that the influences among individuals depending on their roles and the behavior of interest. To operationalize these social science theories, we empirically identify the homophilous communities and use the community structures to capture such “roles”, affecting particular decision-making processes. We propose a generative model named the Stochastic Block influences Model and jointly analyzed both network formation and behavioral influences within and between different empirically-identified communities. To evaluate the performance and demonstrate the interpretability of our method, we study the adoption decisions for a microfinance product in Indian villages. We show that although individuals tend to form links within communities, there are strongly positive and negative social influences between communities, supporting the weak ties theory. Moreover, communities with shared characteristics are associated with positive influences. In contrast, communities that do not overlap are associated with negative influences. Our framework facilitates the quantification of the influences underlying decision communities and is thus a helpful tool for driving information diffusion, viral marketing, and technology adoption. 

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4. Decentralized Online Influence Maximization

   https://doi.org/10.1109/Allerton49937.2022.9929315  

   Bayiz, Yigit E.; Topcu, Ufuk (September 2022, 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton))

   We consider the problem of finding the maximally influential node in random networks where each node influences every other node with constant yet unknown probability. We develop an online algorithm that learns the relative influences of the nodes. It relaxes the assumption in the existing literature that a central observer can monitor the influence spread globally. The proposed algorithm delegates the online updates to the nodes on the network; hence requires only local observations at the nodes. We show that using an explore-then-commit learning strategy, the cumulative regret accumulated by the algorithm over horizon T approaches O(T2/3) for a network with a large number of nodes. Additionally, we show that, for fixed T, the worst case-regret grows linearly with the number n of nodes in the graph. Numerical experiments illustrate this linear dependence for Chung-Lu models. The experiments also demonstrate that ε-greedy learning strategies can achieve similar performance to the explore-then-commit strategy on Chung-Lu models. 

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5. A graph-theoretical basis of stochastic-cascading network influence: Characterizations of influence-based centrality

   https://doi.org/10.1016/j.tcs.2020.04.016  

   Chen, Wei; Teng, Shang-Hua; Zhang, Hairui (January 2020, Theoretical computer science)