In this paper, we study semi-supervised graph classification, which aims at
accurately predicting the categories of graphs in scenarios with limited
labeled graphs and abundant unlabeled graphs. Despite the promising capability
of graph neural networks (GNNs), they typically require a large number of
costly labeled graphs, while a wealth of unlabeled graphs fail to be
effectively utilized. Moreover, GNNs are inherently limited to encoding local
neighborhood information using message-passing mechanisms, thus lacking the
ability to model higher-order dependencies among nodes. To tackle these
challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for
semi-supervised graph classification, which captures graph semantics from the
perspective of the hypergraph and the line graph, respectively. Specifically,
to better explore the higher-order relationships among nodes, we design a
hypergraph structure learning to adaptively learn complex node dependencies
beyond pairwise relations. Meanwhile, based on the learned hypergraph, we
introduce a line graph to capture the interaction between hyperedges, thereby
better mining the underlying semantic structures. Finally, we develop a
relational consistency learning to facilitate knowledge transfer between the
two branches and provide better mutual guidance. Extensive experiments on
real-world graph datasets verify the effectiveness of the proposed method
against existing state-of-the-art methods.

本文研究半监督图分类，在有限标记图和丰富未标记图的场景下准确预测图的类别。通过提出一个名为 HEAL 的超图增强的 DuAL 框架，该框架分别从超图和线图的角度捕捉图的语义，以解决现有图神经网络对大量昂贵标记图的需求以及对节点之间高阶依赖关系建模能力不足的问题。在设计了适应性学习复杂节点依赖关系的超图结构学习的基础上，利用学习到的超图引入线图来捕捉超边之间的交互，从而更好地挖掘潜在的语义结构，并开发了关系一致性学习来促进两个分支之间的知识传递和更好的相互引导。对真实世界的图数据集进行的广泛实验验证了所提方法相对于现有最先进方法的有效性。

超图增强的双半监督图分类

Hypergraph-enhanced Dual Semi-supervised Graph Classification

Unsupervised domain adaptive person re-identification (ReID) has been
extensively investigated to mitigate the adverse effects of domain gaps. Those
works assume the target domain data can be accessible all at once. However, for
the real-world streaming data, this hinders the timely adaptation to changing
data statistics and sufficient exploitation of increasing samples. In this
paper, to address more practical scenarios, we propose a new task, Lifelong
Unsupervised Domain Adaptive (LUDA) person ReID. This is challenging because it
requires the model to continuously adapt to unlabeled data in the target
environments while alleviating catastrophic forgetting for such a fine-grained
person retrieval task. We design an effective scheme for this task, dubbed
CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the
adaptation. Specifically, a meta-based Coordinated Data Replay strategy is
proposed to replay old data and update the network with a coordinated
optimization direction for both adaptation and memorization. Moreover, we
propose Relational Consistency Learning for old knowledge
distillation/inheritance in line with the objective of retrieval-based tasks.
We set up two evaluation settings to simulate the practical application
scenarios. Extensive experiments demonstrate the effectiveness of our
CLUDA-ReID for both scenarios with stationary target streams and scenarios with
dynamic target streams.

本文提出了一种名为 LUDA 的任务，旨在解决实际场景下连续自适应无监督领域人员重识别任务中的遗忘问题和标签任务的一致性问题，并设计了 CLUDA-ReID 方案，其中包括元数据协调数据回放策略和关系一致性学习等技术。