Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications

Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be drawn for pre-training graph models on large graphs in the hope of benefiting downstream graph applications, which has also been explored by several recent studies. However, no existing study has ever investigated the pre-training of text plus graph models on large heterogeneous graphs with abundant textual information (a.k.a. large graph corpora) and then fine-tuning the model on different related downstream applications with different graph schemas. To address this problem, we propose a framework of graph-aware language model pre-training (GALM) on a large graph corpus, which incorporates large language models and graph neural networks, and a variety of fine-tuning methods on downstream applications. We conduct extensive experiments on Amazon's real internal datasets and large public datasets. Comprehensive empirical results and in-depth analysis demonstrate the effectiveness of our proposed methods along with lessons learned.

Semi-Supervised Domain Generalization in Real World:New Benchmark and Strong Baseline

Conventional domain generalization aims to learn domain invariant representation from multiple domains, which requires accurate annotations. In realistic application scenarios, however, it is too cumbersome or even infeasible to collect and annotate the large mass of data. Yet, web data provides a free lunch to access a huge amount of unlabeled data with rich style information that can be harnessed to augment domain generalization ability. In this paper, we introduce a novel task, termed as semi-supervised domain generalization, to study how to interact the labeled and unlabeled domains, and establish two benchmarks including a web-crawled dataset, which poses a novel yet realistic challenge to push the limits of existing technologies. To tackle this task, a straightforward solution is to propagate the class information from the labeled to the unlabeled domains via pseudo labeling in conjunction with domain confusion training. Considering narrowing domain gap can improve the quality of pseudo labels and further advance domain invariant feature learning for generalization, we propose a cycle learning framework to encourage the positive feedback between label propagation and domain generalization, in favor of an evolving intermediate domain bridging the labeled and unlabeled domains in a curriculum learning manner. Experiments are conducted to validate the effectiveness of our framework. It is worth highlighting that web-crawled data benefits domain generalization as demonstrated in our results. Our code will be available later.

Federated Graph Classification over Non-IID Graphs

Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can also be regarded as a special type of data samples, which can be collected and stored in separate local systems. Similar to other domains, multiple local systems, each holding a small set of graphs, may benefit from collaboratively training a powerful graph mining model, such as the popular graph neural networks (GNNs). To provide more motivation towards such endeavors, we analyze real-world graphs from different domains to confirm that they indeed share certain graph properties that are statistically significant compared with random graphs. However, we also find that different sets of graphs, even from the same domain or same dataset, are non-IID regarding both graph structures and node features. To handle this, we propose a graph clustered federated learning (GCFL) framework that dynamically finds clusters of local systems based on the gradients of GNNs, and theoretically justify that such clusters can reduce the structure and feature heterogeneity among graphs owned by the local systems. Moreover, we observe the gradients of GNNs to be rather fluctuating in GCFL which impedes high-quality clustering, and design a gradient sequence-based clustering mechanism based on dynamic time warping (GCFL+). Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed frameworks.