Treating programs as graphs and employing graph learning techniques to analyze them have been widely adopted in many software engineering tasks. A recent progress in this vein is to apply graph neural networks (GNNs) to model program graphs, which is built upon the homophily assumption, i.e., similar nodes tend to connect each other. However, this assumption is not always valid in program graphs, as various edges such as AST edges and token occurrence edges may connect dissimilar nodes with quite different properties. Such phenomenon is termed as the heterophily of program graphs. In this paper, we propose a new heterophily-aware graph convolutional network (HAGCN) to better handle the heterophilic program graphs. Specifically, we first introduce the subtraction operation into the message passing mechanism of GNNs, which allows HAGCN to push apart dissimilar nodes in the representation space. Then, HAGCN separately encodes each type of edges, and uses a global relation-aware attention mechanism to fuse messages from different edge types. Moreover, we also theoretically analyze the expressive power of HAGCN from the perspective of convolution filters and contrast the differences between HAGCN and other GNNs. Finally, we take type inference as an example to evaluate the effectiveness of the proposed approach. Experimental results demonstrate that HAGCN significantly outperforms the existing non-heterophilic competitors, as well as the existing state-of-the-art graph-based type inference approaches.

Personalized recommendation has been instrumental in many real applications. Despite the great progress, the underlying multi-scenario characteristics (e.g., users may behave differently under different scenarios) are largely ignored by existing recommender systems. Intuitively, modeling different scenarios properly could significantly improve the recommendation accuracy, and some existing work has explored this direction. However, these work assumes the scenarios are explicitly given, and thus becomes less effective when such information is unavailable. To complicate things further, proper scenario modeling from data is challenging and the recommendation models may easily overfit to some scenarios. In this paper, we propose a multi-scenario learning framework, MUSENET, for personalized recommendation. The key idea of MUSENET is to learn multiple implicit scenarios from the user behaviors, with a careful design inspired by the causal interpretation of recommender systems to avoid the overfitting issue. Additionally, since users' repeat consumptions account for a large part of the user behavior data on many e-commerce platforms, a repeat-aware mechanism is integrated to handle users' repurchase intentions within each scenario. Comprehensive experimental results on both industrial and public datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art methods.

Graph learning-based collaborative filtering (GLCF), which is built upon the message-passing mechanism of graph neural networks (GNNs), has received great recent attention and exhibited superior performance in recommender systems. However, although GNNs can be easily compromised by adversarial attacks as shown by the prior work, little attention has been paid to the vulnerability of GLCF. Questions like can GLCF models be just as easily fooled as GNNs remain largely unexplored. In this article, we propose to study the vulnerability of GLCF. Specifically, we first propose an adversarial attack against CLCF. Considering the unique challenges of attacking GLCF, we propose to adopt the greedy strategy in searching for the local optimal perturbations and design a reasonable attacking utility function to handle the non-differentiable ranking-oriented metrics. Next, we propose a defense to robustify GCLF. The defense is based on the observation that attacks usually introduce suspicious interactions into the graph to manipulate the message-passing process. We then propose to measure the suspicious score of each interaction and further reduce the message weight of suspicious interactions. We also give a theoretical guarantee of its robustness. Experimental results on three benchmark datasets show the effectiveness of both our attack and defense.