Fake news detection has become a research area that goes way beyond a purely
academic interest as it has direct implications on our society as a whole.
Recent advances have primarily focused on textbased approaches. However, it has
become clear that to be effective one needs to incorporate additional,
contextual information such as spreading behaviour of news articles and user
interaction patterns on social media. We propose to construct heterogeneous
social context graphs around news articles and reformulate the problem as a
graph classification task. Exploring the incorporation of different types of
information (to get an idea as to what level of social context is most
effective) and using different graph neural network architectures indicates
that this approach is highly effective with robust results on a common
benchmark dataset.

通过构建多种异质社交上下文图并将问题重新定义为图分类任务，本文提出了一种高效的方法来检测虚假新闻，并在常见基准数据集上获得了鲁棒的结果，研究成果表明将不同类型的信息（以获得何种社交上下文水平最有效）与使用不同的图形神经网络架构相结合非常有效。

利用异质社交媒体上下文图探究虚假新闻检测

Exploring Fake News Detection with Heterogeneous Social Media Context Graphs

Graph Neural Networks (GNNs) have made tremendous progress in the graph
classification task. However, a performance gap between the training set and
the test set has often been noticed. To bridge such gap, in this work we
introduce the first test-time training framework for GNNs to enhance the model
generalization capacity for the graph classification task. In particular, we
design a novel test-time training strategy with self-supervised learning to
adjust the GNN model for each test graph sample. Experiments on the benchmark
datasets have demonstrated the effectiveness of the proposed framework,
especially when there are distribution shifts between training set and test
set. We have also conducted exploratory studies and theoretical analysis to
gain deeper understandings on the rationality of the design of the proposed
graph test time training framework (GT3).

本研究提出了第一个用于 GNN 的测试时间训练框架，主要包括一种新颖的测试时间训练策略和自监督学习方法，以提高模型的泛化能力，并在基准数据集上进行了实验，证明了该框架的有效性，特别是在训练集与测试集之间存在分布偏移时。同时，研究者还通过探索性研究和理论分析来深入了解了 GT3 框架的设计合理性。

图神经网络的测试时间训练

Test-Time Training for Graph Neural Networks

Graph Neural Networks (GNNs), which generalize deep neural networks to
graph-structured data, have drawn considerable attention and achieved
state-of-the-art performance in numerous graph related tasks. However, existing
GNN models mainly focus on designing graph convolution operations. The graph
pooling (or downsampling) operations, that play an important role in learning
hierarchical representations, are usually overlooked. In this paper, we propose
a novel graph pooling operator, called Hierarchical Graph Pooling with
Structure Learning (HGP-SL), which can be integrated into various graph neural
network architectures. HGP-SL incorporates graph pooling and structure learning
into a unified module to generate hierarchical representations of graphs. More
specifically, the graph pooling operation adaptively selects a subset of nodes
to form an induced subgraph for the subsequent layers. To preserve the
integrity of graph's topological information, we further introduce a structure
learning mechanism to learn a refined graph structure for the pooled graph at
each layer. By combining HGP-SL operator with graph neural networks, we perform
graph level representation learning with focus on graph classification task.
Experimental results on six widely used benchmarks demonstrate the
effectiveness of our proposed model.

本文提出了一种新的 Hierarchical Graph Pooling with Structure Learning (HGP-SL) 图池化算子来生成图结构的分层表示，并将其与图神经网络相结合，专注于图分类任务。通过实验结果表明，这种方法在六个广泛应用的基准测试中均取得了良好的效果。