The goal of this project is to create and study novel techniques to identify
early warning signals for socially disruptive events, like riots, wars, or
revolutions using only publicly available data on social media. Such techniques
need to be robust enough to work on real-time data: to achieve this goal we
propose a topological approach together with more standard BERT models. Indeed,
topology-based algorithms, being provably stable against deformations and
noise, seem to work well in low-data regimes. The general idea is to build a
binary classifier that predicts if a given tweet is related to a disruptive
event or not. The results indicate that the persistent-gradient approach is
stable and even more performant than deep-learning-based anomaly detection
algorithms. We also benchmark the generalisability of the methodology against
out-of-samples tasks, with very promising results.

本项目的目标是通过使用社交媒体上的公开可用数据来创建和研究识别早期警报信号的新技术，这些信号预示着社会失序事件，如暴动、战争或革命。该研究的一般想法是建立一个二元分类器，预测给定的推文是否与破坏性事件有关。结果表明，基于拓扑的算法稳定性较高，比基于深度学习的异常检测算法表现更好。我们还对方法的普适性进行了测试，结果非常有前途。

推特数据中社会动荡的预警信号

Early Warning Signals of Social Instabilities in Twitter Data

Link prediction is an important learning task for graph-structured data. In
this paper, we propose a novel topological approach to characterize
interactions between two nodes. Our topological feature, based on the extended
persistent homology, encodes rich structural information regarding the
multi-hop paths connecting nodes. Based on this feature, we propose a graph
neural network method that outperforms state-of-the-arts on different
benchmarks. As another contribution, we propose a novel algorithm to more
efficiently compute the extended persistence diagrams for graphs. This
algorithm can be generally applied to accelerate many other topological methods
for graph learning tasks.

该研究提出了一种基于扩展持久同调的拓扑特征，以编码连接节点的多跳路径的丰富的结构信息，并提出了一种图神经网络方法，优于不同基准的现有技术。此外，该研究还提出了一种更高效地计算扩展持久性图的算法，可以普遍应用于加速图学习任务中的许多其他拓扑方法。

基于持久同调的链路预测：交互视角

Link Prediction with Persistent Homology: An Interactive View

We address the problem of crowd localization, i.e., the prediction of dots
corresponding to people in a crowded scene. Due to various challenges, a
localization method is prone to spatial semantic errors, i.e., predicting
multiple dots within a same person or collapsing multiple dots in a cluttered
region. We propose a topological approach targeting these semantic errors. We
introduce a topological constraint that teaches the model to reason about the
spatial arrangement of dots. To enforce this constraint, we define a
persistence loss based on the theory of persistent homology. The loss compares
the topographic landscape of the likelihood map and the topology of the ground
truth. Topological reasoning improves the quality of the localization algorithm
especially near cluttered regions. On multiple public benchmarks, our method
outperforms previous localization methods. Additionally, we demonstrate the
potential of our method in improving the performance in the crowd counting
task.

本文提出一种基于拓扑学方法的众包定位算法，引入一种基于持久同调理论的持久损失函数，通过对空间点的拓扑约束来避免语义错误，提高了在拥挤区域中的定位精度，并在公共基准测试中表现优越，同时还对人群分布的研究有着潜在的拓展应用。