7,685 research outputs found
Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks
Detecting spreading outbreaks in social networks with sensors is of great
significance in applications. Inspired by the formation mechanism of human's
physical sensations to external stimuli, we propose a new method to detect the
influence of spreading by constructing excitable sensor networks. Exploiting
the amplifying effect of excitable sensor networks, our method can better
detect small-scale spreading processes. At the same time, it can also
distinguish large-scale diffusion instances due to the self-inhibition effect
of excitable elements. Through simulations of diverse spreading dynamics on
typical real-world social networks (facebook, coauthor and email social
networks), we find that the excitable senor networks are capable of detecting
and ranking spreading processes in a much wider range of influence than other
commonly used sensor placement methods, such as random, targeted, acquaintance
and distance strategies. In addition, we validate the efficacy of our method
with diffusion data from a real-world online social system, Twitter. We find
that our method can detect more spreading topics in practice. Our approach
provides a new direction in spreading detection and should be useful for
designing effective detection methods
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning
It has always been an important yet challenging problem to control language
models to avoid generating texts with undesirable attributes, such as toxic
language and unnatural repetition. We introduce Click for controllable text
generation, which needs no modification to the model architecture and
facilitates out-of-the-box use of trained models. It employs a contrastive loss
on sequence likelihood, which fundamentally decreases the generation
probability of negative samples (i.e., generations with undesirable
attributes). It also adopts a novel likelihood ranking-based strategy to
construct contrastive samples from model generations. On the tasks of language
detoxification, sentiment steering, and repetition reduction, we show that
Click outperforms strong baselines of controllable text generation and
demonstrate the superiority of Click's sample construction strategy.Comment: Findings of ACL 202
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