Uncovering rationales behind predictions of graph neural networks (GNNs) has
received increasing attention over the years. Existing literature mainly focus
on selecting a subgraph, through combinatorial optimization, to provide
faithful explanations. However, the exponential size of candidate subgraphs
limits the applicability of state-of-the-art methods to large-scale GNNs. We
enhance on this through a different approach: by proposing a generative
structure -- GFlowNets-based GNN Explainer (GFlowExplainer), we turn the
optimization problem into a step-by-step generative problem. Our GFlowExplainer
aims to learn a policy that generates a distribution of subgraphs for which the
probability of a subgraph is proportional to its' reward. The proposed approach
eliminates the influence of node sequence and thus does not need any
pre-training strategies. We also propose a new cut vertex matrix to efficiently
explore parent states for GFlowNets structure, thus making our approach
applicable in a large-scale setting. We conduct extensive experiments on both
synthetic and real datasets, and both qualitative and quantitative results show
the superiority of our GFlowExplainer.

本研究提出了一种基于 GFlowNets 的 GNN Explainer（GFlowExplainer）方法，通过生成结构而非组合优化的方法来生成子图，加快对 GNN 预测结果的解释，并在综合性实验中证明了其优越性。

DAG Matters! GFlowNets 增强图神经网络的解释器

DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks

Contrastive learning has shown great promise in the field of graph
representation learning. By manually constructing positive/negative samples,
most graph contrastive learning methods rely on the vector inner product based
similarity metric to distinguish the samples for graph representation. However,
the handcrafted sample construction (e.g., the perturbation on the nodes or
edges of the graph) may not effectively capture the intrinsic local structures
of the graph. Also, the vector inner product based similarity metric cannot
fully exploit the local structures of the graph to characterize the graph
difference well. To this end, in this paper, we propose a novel adaptive
subgraph generation based contrastive learning framework for efficient and
robust self-supervised graph representation learning, and the optimal transport
distance is utilized as the similarity metric between the subgraphs. It aims to
generate contrastive samples by capturing the intrinsic structures of the graph
and distinguish the samples based on the features and structures of subgraphs
simultaneously. Specifically, for each center node, by adaptively learning
relation weights to the nodes of the corresponding neighborhood, we first
develop a network to generate the interpolated subgraph. We then construct the
positive and negative pairs of subgraphs from the same and different nodes,
respectively. Finally, we employ two types of optimal transport distances
(i.e., Wasserstein distance and Gromov-Wasserstein distance) to construct the
structured contrastive loss. Extensive node classification experiments on
benchmark datasets verify the effectiveness of our graph contrastive learning
method.

本研究提出了一种基于自适应子图生成对比学习框架，可以通过捕捉图的内在结构来生成对比样本，并同时根据子图的特征和结构来区分样本，以最优运输距离作为子图之间的相似度度量。通过在基准数据集上进行广泛的节点分类实验，验证了该图对比学习方法的有效性。