02.01.2024

Three Papers accepted at AAAI, AAMAS and IPEC

For the start of the year we are proud to announce three accepted papers! The first paper was accepted at the 38th Annual AAAI Conference on Artificial Intelligence which had an overall acceptance rate of 24%! The conference will take place in Vancouver, Canada from 20-27 February. The paper about the SIS process is titled The Irrelevance of Influencers: Information Diffusion with Re-Activation and Immunity Lasts Exponentially Long on Social Networks. The SIS process is a well known model for infections in networks in which the nodes can be either susceptible or infected. The paper theoretically analyzes how this model changes when adding a recovered period after each infection in which the nodes are immune to reinfections. It gives bounds on the infection rate that is needed in various graph models in order for infections to become epidemic.

The second paper From Market Saturation to Social Reinforcement: Understanding the Impact of Non-Linearity in Information Diffusion Models was accepted at the 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) which will take place in Auckland, New Zealand on 6-10 May. The paper is about diffusion processes model phenomena such as rumor spreading and people affected by marketing campaigns. Normally these processes are modelled as infections spreading in graphs in which new nodes get infected at a rate linear in the number of their infected neighbors. The paper theoretically analyzes how the behavior of such processes change if this rate is sub- or superlinear instead. This models effects such as market saturation and social reinforcement.

Finally, at the International Symposium on Parameterized and Exact Computation (IPEC), the paper Consistency-Checking Problems: A Gateway to Parameterized Sample Complexity was accepted and was presented in Amsterdam, Netherlands on 6-8 September. Any classical search problem admits a "consistency-checking" version where instead of receiving a single instance, one receives multiple yes- and no-examples and is tasked with finding a solution which is consistent with the provided examples. Consistency-checking problems occur naturally in the context of PAC-learning, however they are almost entirely unexplored from a parameterized complexity perspective. In this article, we provide an overview of these problems and their parameterized complexity classification, showing in particular that their behavior is surprisingly different from that of classical decision problems.

Clean Citation Style 005

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Friedrich, Tobias; Göbel, Andreas; Klodt, Nicolas; Krejca, Martin S.; Pappik, Marcus The Irrelevance of Influencers: Information Diffusion with Re-Activation and Immunity Lasts Exponentially Long on Social Network ModelsAnnual AAAI Conference on Artificial Intelligence 2024

[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ]

@inproceedings{friedrich2024irrelevance,
  abstract = {Information diffusion models on networks are at the forefront of AI research. The dynamics of such models typically follow stochastic models from epidemiology, used to model not only infections but various phenomena, including the behavior of computer viruses and viral marketing campaigns. A core question in this setting is how to efficiently detect the most influential vertices in the host graph such that the infection survives the longest. In processes that incorporate re-infection of the vertices, such as the SIS process, theoretical studies identify parameter thresholds where the survival time of the process rapidly transitions from logarithmic to super-polynomial. These results contradict the intuition that the starting configuration is relevant, since the process will always either die out fast or survive almost indefinitely. A shortcoming of these results is that models incorporating short-term immunity (or creative advertisement fatigue) have not been subjected to such a theoretical analysis so far. We reduce this gap in the literature by studying the SIRS process, a more realistic model, which besides re-infection additionally incorporates short-term immunity. On complex network models, we identify parameter regimes for which the process survives exponentially long, and we get a tight threshold for random graphs. Underlying these results is our main technical contribution, showing a threshold behavior for the survival time of the SIRS process on graphs with large expander subgraphs, such as social network models.},
  author = {Friedrich, Tobias and Göbel, Andreas and Klodt, Nicolas and Krejca, Martin S. and Pappik, Marcus},
  booktitle = {Annual AAAI Conference on Artificial Intelligence},
  keywords = {nicolasklodt marcuspappik martinskrejca andreasgoebel tobiasfriedrich aaai year2024},
  title = {The Irrelevance of Influencers: Information Diffusion with Re-Activation and Immunity Lasts Exponentially Long on Social Network Models},
  year = 2024
}

Clean Citation Style 005

Friedrich, Tobias; Göbel, Andreas; Klodt, Nicolas; Krejca, Martin S.; Pappik, Marcus From Market Saturation to Social Reinforcement: Understanding the Impact of Non-Linearity in Information Diffusion ModelsThe 23rd International Conference on Autonomous Agents and Multi-Agent Systems 2024

[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ]

@inproceedings{friedrich2024market,
  abstract = {Diffusion of information in networks is at the core of many problems in AI. Common examples include the spread of ideas and rumors as well as marketing campaigns. Typically, information diffuses at a non-linear rate, for example, if markets become saturated or if users of social networks reinforce each other's opinions. Despite these characteristics, this area has seen little research, compared to the vast amount of results for linear models, which exhibit less complex dynamics. Especially, when considering the possibility of re-infection, no fully rigorous guarantees exist so far. We address this shortcoming by studying a very general non-linear diffusion model that captures saturation as well as reinforcement. More precisely, we consider a variant of the SIS model in which vertices get infected at a rate that scales polynomially in the number of their infected neighbors, weighted by an infection coefficient \(\lambda\). We give the first fully rigorous results for thresholds of \(\lambda\) at which the expected survival time becomes super-polynomial. For cliques we show that when the infection rate scales sub-linearly, the threshold only shifts by a poly-logarithmic factor, compared to the standard SIS model. In contrast, super-linear scaling changes the process considerably and shifts the threshold by a polynomial term. For stars, sub-linear and super-linear scaling behave similar and both shift the threshold by a polynomial factor. Our bounds are almost tight, as they are only apart by at most a poly-logarithmic factor from the lower thresholds, at which the expected survival time is logarithmic.},
  author = {Friedrich, Tobias and Göbel, Andreas and Klodt, Nicolas and Krejca, Martin S. and Pappik, Marcus},
  booktitle = {The 23rd International Conference on Autonomous Agents and Multi-Agent Systems},
  keywords = {aamas andreasgoebel marcuspappik martinskrejca nicolasklodt tobiasfriedrich year2024},
  title = {From Market Saturation to Social Reinforcement: Understanding the Impact of Non-Linearity in Information Diffusion Models},
  year = 2024
}

Clean Citation Style 005

Ganian, Robert; Khazaliya, Liana; Simonov, Kirill Consistency Checking Problems: A Gateway to Parameterized Sample ComplexityInternational Symposium on Parameterized and Exact Computation (IPEC) 2023: 18:1–18:17

[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ]