Time series classification is a widely studied problem in the field of time
series data mining. Previous research has predominantly focused on scenarios
where relevant or foreground subsequences have already been extracted, with
each subsequence corresponding to a single label. However, real-world time
series data often contain foreground subsequences that are intertwined with
background subsequences. Successfully classifying these relevant subsequences
requires not only distinguishing between different classes but also accurately
identifying the foreground subsequences amidst the background. To address this
challenge, we propose a novel subsequence classification method that represents
each subsequence as an ego-network, providing crucial nearest neighbor
information to the model. The ego-networks of all subsequences collectively
form a time series subsequence graph, and we introduce an algorithm to
efficiently construct this graph. Furthermore, we have demonstrated the
significance of enforcing temporal consistency in the prediction of adjacent
subsequences for the subsequence classification problem. To evaluate the
effectiveness of our approach, we conducted experiments using 128 univariate
and 30 multivariate time series datasets. The experimental results demonstrate
the superior performance of our method compared to alternative approaches.
Specifically, our method outperforms the baseline on 104 out of 158 datasets.

我们提出了一种新颖的子序列分类方法，将每个子序列表示为一个自我网络，为模型提供关键的最近邻信息，通过构建时间序列子序列图并强调相邻子序列的时间一致性，在 128 个单变量和 30 个多变量时间序列数据集上实验证明了我们方法的卓越性能，优于其他方法。

时间序列数据中的子序列分类的自我网络变换器

Ego-Network Transformer for Subsequence Classification in Time Series  Data

Interactions involving multiple objects simultaneously are ubiquitous across
many domains. The systems these interactions inhabit can be modelled using
hypergraphs, a generalization of traditional graphs in which each edge can
connect any number of nodes. Analyzing the global and static properties of
these hypergraphs has led to a plethora of novel findings regarding how these
modelled systems are structured. However, less is known about the localized
structure of these systems and how they evolve over time. In this paper, we
propose the study of hypergraph ego-networks, a structure that can be used to
model higher-order interactions involving a single node. We also propose the
temporal reconstruction of hypergraph ego-networks as a benchmark problem for
models that aim to predict the local temporal structure of hypergraphs. By
combining a deep learning binary classifier with a hill-climbing algorithm, we
will present a model for reconstructing hypergraph ego-networks by
incorporating structural patterns found across multiple domains.

本文提出了用超图的 ego 网络结构建模高阶单节点交互，并提出了超图 ego 网络的时间重构作为预测超图局部时空结构的模型的基准问题。研究者们将深度学习二元分类器与爬山算法相结合，以跨多种领域找到的结构模式为基础，提出了重构超图 ego 网络的模型。

超图自我网络及其时间演化

Hypergraph Ego-networks and Their Temporal Evolution

Ego-networks are fundamental structures in social graphs, yet the process of
their evolution is still widely unexplored. In an online context, a key
question is how link recommender systems may skew the growth of these networks,
possibly restraining diversity. To shed light on this matter, we analyze the
complete temporal evolution of 170M ego-networks extracted from Flickr and
Tumblr, comparing links that are created spontaneously with those that have
been algorithmically recommended. We find that the evolution of ego-networks is
bursty, community-driven, and characterized by subsequent phases of explosive
diameter increase, slight shrinking, and stabilization. Recommendations favor
popular and well-connected nodes, limiting the diameter expansion. With a
matching experiment aimed at detecting causal relationships from observational
data, we find that the bias introduced by the recommendations fosters global
diversity in the process of neighbor selection. Last, with two link prediction
experiments, we show how insights from our analysis can be used to improve the
effectiveness of social recommender systems.

通过分析来自 Flickr 和 Tumblr 的 170M 个自我网络的完整时间演变，我们发现自我网络的演变是爆发性的、社区驱动的，有后续的直径增加、轻微收缩和稳定阶段。 推荐系统有利于流行和连接紧密的节点，限制了直径的展开。 推荐系统引入的偏差促进了邻居选择的全局多样性。最后，我们通过两个链接预测实验展示了如何利用我们分析的见解来提高社交推荐系统的效果。