Kriging aims at estimating the attributes of unsampled geo-locations from
observations in the spatial vicinity or physical connections, which helps
mitigate skewed monitoring caused by under-deployed sensors. Existing works
assume that neighbors' information offers the basis for estimating the
attributes of the unobserved target while ignoring non-neighbors. However,
non-neighbors could also offer constructive information, and neighbors could
also be misleading. To this end, we propose ``Contrastive-Prototypical''
self-supervised learning for Kriging (KCP) to refine valuable information from
neighbors and recycle the one from non-neighbors. As a pre-trained paradigm, we
conduct the Kriging task from a new perspective of representation: we aim to
first learn robust and general representations and then recover attributes from
representations. A neighboring contrastive module is designed that coarsely
learns the representations by narrowing the representation distance between the
target and its neighbors while pushing away the non-neighbors. In parallel, a
prototypical module is introduced to identify similar representations via
exchanged prediction, thus refining the misleading neighbors and recycling the
useful non-neighbors from the neighboring contrast component. As a result, not
all the neighbors and some of the non-neighbors will be used to infer the
target. To encourage the two modules above to learn general and robust
representations, we design an adaptive augmentation module that incorporates
data-driven attribute augmentation and centrality-based topology augmentation
over the spatiotemporal Kriging graph data. Extensive experiments on real-world
datasets demonstrate the superior performance of KCP compared to its peers with
6% improvements and exceptional transferability and robustness. The code is
available at this https URL

通过邻域对比学习和原型学习优化 Kriging 方法，从邻居中提取有价值的信息并从非邻居中重新利用有用信息，以实现准确估计未采样地理位置的属性。

非邻居也在克里金中起作用：一种新的对比原型学习

Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical  Learning

Temporal graph representation learning aims to generate low-dimensional
dynamic node embeddings to capture temporal information as well as structural
and property information. Current representation learning methods for temporal
networks often focus on capturing fine-grained information, which may lead to
the model capturing random noise instead of essential semantic information.
While graph contrastive learning has shown promise in dealing with noise, it
only applies to static graphs or snapshots and may not be suitable for handling
time-dependent noise. To alleviate the above challenge, we propose a novel
Temporal Graph representation learning with Adaptive augmentation Contrastive
(TGAC) model. The adaptive augmentation on the temporal graph is made by
combining prior knowledge with temporal information, and the contrastive
objective function is constructed by defining the augmented inter-view contrast
and intra-view contrast. To complement TGAC, we propose three adaptive
augmentation strategies that modify topological features to reduce noise from
the network. Our extensive experiments on various real networks demonstrate
that the proposed model outperforms other temporal graph representation
learning methods.

提出了一种新颖的 Temporal Graph representation learning with Adaptive augmentation Contrastive (TGAC) 模型，该模型通过将先验知识与时间信息相结合，对时态图进行自适应增强，并通过定义增强之间的相互视角对比和内部视角对比来构建对比目标函数，以减少网络中的噪声。广泛的实验证明，该模型优于其他时态图表示学习方法。

自适应增强对比的时态图表示学习

Temporal Graph Representation Learning with Adaptive Augmentation  Contrastive

Recent studies show that graph neural networks (GNNs) are prevalent to model
high-order relationships for collaborative filtering (CF). Towards this
research line, graph contrastive learning (GCL) has exhibited powerful
performance in addressing the supervision label shortage issue by learning
augmented user and item representations. While many of them show their
effectiveness, two key questions still remain unexplored: i) Most existing
GCL-based CF models are still limited by ignoring the fact that user-item
interaction behaviors are often driven by diverse latent intent factors (e.g.,
shopping for family party, preferred color or brand of products); ii) Their
introduced non-adaptive augmentation techniques are vulnerable to noisy
information, which raises concerns about the model's robustness and the risk of
incorporating misleading self-supervised signals. In light of these
limitations, we propose a Disentangled Contrastive Collaborative Filtering
framework (DCCF) to realize intent disentanglement with self-supervised
augmentation in an adaptive fashion. With the learned disentangled
representations with global context, our DCCF is able to not only distill
finer-grained latent factors from the entangled self-supervision signals but
also alleviate the augmentation-induced noise. Finally, the cross-view
contrastive learning task is introduced to enable adaptive augmentation with
our parameterized interaction mask generator. Experiments on various public
datasets demonstrate the superiority of our method compared to existing
solutions. Our model implementation is released at the link
this https URL

本篇研究提出了一种基于图神经网络和图对比学习的协作过滤框架，通过自我监督学习进行自适应增强，实现目的的解耦和噪声抑制，将学习到的解耦表示与全局上下文结合，相比于现有的解决方案，具有更好的性能表现。