@inproceedings {43328,
	title = {A Time-aware Tensor Decomposition for Tracking Evolving Patterns},
	journal = {MLSP{\textquoteright}23:  IEEE International Workshop on Machine Learning for Signal Processing},
	year = {2023},
	publisher = { IEEE},
	author = {Chatzis, Christos and Pfeffer, Max and Lind, Pedro and Evrim Acar {Ataman}}
}
@article {28485,
	title = {The connectivity network underlying the German{\textquoteright}s Twittersphere: a testbed for investigating information spreading phenomena},
	journal = {Scientific Reports},
	volume = {12},
	year = {2022},
	month = {Jan-12-2022},
	publisher = {Nature Publishing Group},
	abstract = {Online social networks are ubiquitous, have billions of users, and produce large amounts of data. While platforms like Reddit are based on a forum-like organization where users gather around topics, Facebook and Twitter implement a concept in which individuals represent the primary entity of interest. This makes them natural testbeds for exploring individual behavior in large social networks. Underlying these individual-based platforms is a network whose {\textquotedblleft}friend{\textquotedblright} or {\textquotedblleft}follower{\textquotedblright} edges are of binary nature only and therefore do not necessarily reflect the level of acquaintance between pairs of users. In this paper,we present the network of acquaintance {\textquotedblleft}strengths{\textquotedblright} underlying the German Twittersphere. To that end, we make use of the full non-verbal information contained in tweet{\textendash}retweet actions to uncover the graph of social acquaintances among users, beyond pure binary edges. The social connectivity between pairs of users is weighted by keeping track of the frequency of shared content and the time elapsed between publication and sharing. Moreover, we also present a preliminary topological analysis of the German Twitter network. Finally, making the data describing the weighted German Twitter network of acquaintances, we discuss how to apply this framework as a ground basis for investigating spreading phenomena of particular contents.},
	doi = {10.1038/s41598-022-07961-3},
	url = {https://www.nature.com/articles/s41598-022-07961-3},
	author = {Daniel Thilo {Schroeder} and Langguth, Johannes and Burchard, Luk and Pogorelov, Konstantin and Pedro G. {Lind}}
}
@inproceedings {pontes2020evodynamic,
	title = {EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality},
	journal = {International Conference on the Applications of Evolutionary Computation (Part of EvoStar)},
	year = {2020},
	pages = {133{\textendash}148},
	publisher = {Springer},
	address = {Cham},
	author = {Pontes-Filho, Sidney and Lind, Pedro and Yazidi, Anis and Zhang, Jianhua and Hugo Lewi {Hammer} and Gustavo BM {Mello} and Sandvig, Ioanna and Tufte, Gunnar and Nichele, Stefano}
}
@misc {27673,
	title = {A Framework for Interaction-based Propagation Analysis in Online Social Networks},
	year = {2020},
	address = {Complex Networks},
	author = {Daniel Thilo {Schroeder} and Lind, Pedro and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {27564,
	title = {A Framework for Interaction-based Propagation Analysis in Online Social Networks},
	journal = {Complex Networks},
	year = {2020},
	note = {Extended abstract},
	publisher = {Springer},
	abstract = {Online social networks create a digital footprint of human interaction naturally by the way they function. Thus, they allow a large-scale analysis of human behavior which was previously infeasible for social scientists. Consequently, social networks have been studied intensely in the last decade. The core of most social networks is the relationship between users which can be described as a graph. The graph can be either undirected, as is the case for the friendship relation of Facebook, or directed, which is the case of the follower relation on Twitter. The relationship is readily visible, e.g. on the user interface of the social networks themselves. However, these edges are unweighted expressions of interest and reflect how individuals have chosen to relate to each other rather than how they actually interact with each other. For studying information propagation, comparing interaction properties is crucial and, therefore, using models based on connections that reflect different dimensions and strengths of acquaintance seems appropriate. Thus, there is a need for obtaining weighted edges from the communication that occurs on the social network. In this paper, we present a novel method to calculate an acquaintance score between pairs of Twitter users and use the resulting networks to enable the analysis of interaction based information propagation. By understanding the frequency and velocity with which individuals share content as a measure of acquaintance, it becomes possible to predict, compare communication patterns, and detect unusual communication. In contrast to previous work which assigns edge weights based on tie strength, our score considers the response time as a crucial factor and, therefore, enables time-based spreading comparisons.},
	author = {Daniel Thilo {Schroeder} and Lind, Pedro and Pogorelov, Konstantin and Langguth, Johannes}
}
@article {pontes2020neuro,
	title = {A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality},
	journal = {Cognitive Neurodynamics},
	volume = {14},
	year = {2020},
	pages = {1{\textendash}18},
	publisher = {Springer},
	url = {https://doi.org/10.1007/s11571-020-09600-x},
	author = {Pontes-Filho, Sidney and Lind, Pedro and Yazidi, Anis and Zhang, Jianhua and Hugo Lewi {Hammer} and Gustavo BM {Mello} and Sandvig, Ioanna and Tufte, Gunnar and Nichele, Stefano}
}
