We present a study using new computational methods, based on a novel
combination of machine learning for inferring admixture hidden Markov models
and probabilistic model checking, to uncover interaction styles in a mobile
app. These styles are then used to inform a redesign, which is implemented,
deployed, and then analysed using the same methods. The data sets are logged
user traces, collected over two six-month deployments of each version,
involving thousands of users and segmented into different time intervals. The
methods do not assume tasks or absolute metrics such as measures of engagement,
but uncover the styles through unsupervised inference of clusters and analysis
with probabilistic temporal logic. For both versions there was a clear
distinction between the styles adopted by users during the first day/week/month
of usage, and during the second and third months, a result we had not
anticipated.

本研究使用机器学习及概率模型检验等计算方法，以用户行为追踪数据为基础，无需任务或绝对度量，无监督地推断出移动应用中的交互样式，并通过重新设计和实现来优化其用户体验。

概率形式建模揭示和解释交互方式

Probabilistic Formal Modelling to Uncover and Interpret Interaction  Styles

Graph neural networks (GNNs) mainly rely on the message-passing paradigm to
propagate node features and build interactions, and different graph learning
tasks require different ranges of node interactions. In this work, we explore
the capacity of GNNs to capture interactions between nodes under contexts with
different complexities. We discover that GNNs are usually unable to capture the
most informative kinds of interaction styles for diverse graph learning tasks,
and thus name this phenomenon as GNNs' representation bottleneck. As a
response, we demonstrate that the inductive bias introduced by existing graph
construction mechanisms can prevent GNNs from learning interactions of the most
appropriate complexity, i.e., resulting in the representation bottleneck. To
address that limitation, we propose a novel graph rewiring approach based on
interaction patterns learned by GNNs to adjust the receptive fields of each
node dynamically. Extensive experiments on both real-world and synthetic
datasets prove the effectiveness of our algorithm to alleviate the
representation bottleneck and its superiority to enhance the performance of
GNNs over state-of-the-art graph rewiring baselines.

本文探讨了图神经网络在不同复杂情况下捕捉节点之间相互作用信息的能力，并发现了当前机制存在的问题，提出了一种基于交互模式的新颖图重连方法以缓解表征瓶颈并提高性能。实验结果证明了该方法的有效性及其优于现有基准算法的表现。