Graph Neural Networks (GNNs) have gained considerable traction for modeling molecular structures and predicting properties, but their interpretability remains a significant challenge in understanding chemical behaviors. Current interpretation methods often rely on post-hoc explanations, which aim to provide transparency in GNN decisions. However, these approaches struggle with interpreting complex subgraphs and fail to leverage explanations to enhance predictive capabilities. While transparent methods can enhance GNN predictions, they typically compromise on explanation precision. This limitation underscores the need for a new strategy that effectively integrates GNN explanations and predictions. In this study, we have developed a novel interpretable causal GNN framework that combines retrieval-based causal learning with Graph Information Bottleneck (GIB) theory. Our framework semi-parametrically identifies crucial subgraphs through GIB and compresses explanatory subgraphs using a causal module. The framework consistently outperformed state-of-the-art methods, achieving a 32.72% increase in precision for scientific explanation tasks involving diverse substructures. More importantly, the learned explanations were also shown to be able to improve GNN prediction performance. This advancement is particularly vital for molecular graph learning, as it addresses the critical need to interpret how molecular structures influence predicted properties, thereby aiding drug discovery and materials science by providing insights into chemical mechanisms.

With the increasing application of deep learning to solve scientific problems in biochemistry, molecular federated learning has become popular due to its ability to offer distributed privacy-preserving solutions. However, most existing molecular federated learning methods rely on joint training with public datasets, which are difficult to obtain in practice. These methods also fail to leverage multi-modal molecular representations effectively. To address the above issues, we propose a novel framework, Federated Heterogeneous Contrastive Distillation (FedHCD), which enables to jointly train global models from clients with heterogeneous data modalities, learning tasks, and molecular models. To aggregate data representations of different modalities in a data-free manner, we design a global multi-modal contrastive strategy to align the representation of clients without public dataset. Utilizing intrinsic characteristics of molecular data in different modalities, we tackle the exacerbation of local model drift and data Non-IIDness caused by multi-modal clients. We introduce a multi-view contrastive knowledge transfer to extract features from atoms, substructures, and molecules, solving the issue of information distillation failure due to dimensional biases in different data modalities. Our evaluations on eight real-world molecular datasets and ablation experiments show that FedHCD outperforms other state-of-the-art FL methods, irrespective of whether or not they use public datasets.

Recently, how the model performance scales with the training sample size has been extensively studied for large models on vision and language related domains. Nevertheless, the ubiquitous node classification tasks on web-scale graphs were ignored, where the traits of these tasks, such as non-IIDness and transductive setting, are likely to cause different scaling laws and motivate novel techniques to beat the law. Therefore, we first explore the neural scaling law for node classification tasks on three large-scale graphs. Then, we benchmark several state-of-the-art data pruning methods on these tasks, not only validating the possibility of improving the original unsatisfactory power law but also gaining insights into a hard-and-representative principle on picking an effective subset of training nodes. Moreover, we leverage the transductive setting to propose a novel data pruning method, which instantiates our principle in a test set-targeted manner. Our method consistently outperforms related methods on all three datasets. Meanwhile, we utilize a PAC-Bayesian framework to analyze our method, extending prior results to account for both hardness and representativeness. In addition to a promising way to ease GNN training on web-scale graphs, our study offers knowledge of the relationship between training nodes and GNN generalization.

The successes of applying Transformer to graphs have been witnessed on small graphs (e.g., molecular graphs), yet two barriers prevent its adoption on large graphs (e.g., citation networks). First, despite the benefit of the global receptive field, enormous distant nodes might distract the necessary attention of each target node from its neighborhood. Second, training a Transformer model on large graphs is costly due to the node-to-node attention mechanism's quadratic computational complexity. To break down these barriers, we propose a two-view architectureCoarformer, wherein a GNN-based module captures fine-grained local information from the original graph, and a Transformer-based module captures coarse yet long-range information on the coarse graph. We further design a cross-view propagation scheme so that these two views can enhance each other. Our graph isomorphism analysis shows the complementary natures of GNN and Transformer, justifying the motivation and design ofCoarformer. We conduct extensive experiments on real-world datasets, whereCoarformersurpasses any single-view method that solely applies a GNN or Transformer. As an ablation,Coarformeroutperforms straightforward combinations of a GNN model and a Transformer-based model, verifying the effectiveness of our coarse global view and the cross-view propagation scheme. Meanwhile,Coarformerconsumes the least runtime and GPU memory than those combinations.

Nowadays, as privacy concerns continue to rise, federated graph learning (FGL) which generalizes the classic federated learning to graph data has attracted increasing attention. However, while the focus has been on designing collaborative learning algorithms, the potential risks of privacy leakage through the sharing of necessary graph-related information in FGL, such as node embeddings and neighbor generators, have been largely neglected. In this paper, we verify the potential risks of privacy leakage in FGL, and provide insights about the cautions in FGL algorithm design. Specifically, we propose a novel privacy attack algorithm named Privacy Attack on federated Graph learning (PAG) towards reconstructing participants’ private node attributes and the linkage relationships. The participant performing the PAG attack is able to reconstruct the node attributes of the victim by matching the received gradients of the generator, and then train a link prediction model based on its local sub-graph to inductively infer the linkages connected to these reconstructed nodes. We theoretically and empirically demonstrate that under PAG attack, directly sharing the neighbor generators makes the FGL vulnerable to the data reconstruction attack. Furthermore, an investigation into the key factors that can hinder the success of the PAG attack provides insights into corresponding defense strategies and inspires future research into privacy-preserving FGL.

Some recent works have shown the advantages of reinforcement learning (RL) based learned query optimizers. These works often use the cost (i.e., the estimation of cost model) or the latency (i.e., execution time) as guidance signals for training their learned models. However, cost-based learning underperforms in latency and latency-based learning is time-intensive. In order to bypass such a dilemma, researchers attempt to transfer a learned value network from the cost domain to the latency domain. We recognize critical insights in cost/latency-based training, prompting us to transfer the reward function rather than the value network. Based on this idea, we propose a two-stage RL-based framework, BASE , to bridge the gap between cost and latency. After learning a policy based on cost signals in its first stage, BASE formulates transferring the reward function as a variant of inverse reinforcement learning. Intuitively, BASE learns to calibrate the reward function and updates the policy regarding the calibrated one in a mutually-improved manner. Extensive experiments exhibit the superiority of BASE on two benchmark datasets: Our optimizer outperforms traditional DBMS, using 30% less training time than SOTA methods. Meanwhile, our approach can enhance the efficiency of other learning-based optimizers.

Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity. To fill this gap, in this paper, we propose a novel FL platform, named FederatedScope, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants. Such a design makes it easy for users to describe participants with various local training processes, learning goals and backends, and coordinate them into an FL course with synchronous or asynchronous training strategies. Towards an easy-to-use and flexible platform, FederatedScope enables rich types of plug-in operations and components for efficient further development, and we have implemented several important components to better help users with privacy protection, attack simulation and auto-tuning. We have released FederatedScope at https://github.com/alibaba/FederatedScope to promote academic research and industrial deployment of federated learning in a wide range of scenarios.

Masked autoencoder (MAE), as an effective self-supervised learner for computer vision and natural language processing, has been recently applied to molecule representation learning. In this paper, we identify two issues in applying MAE to pre-train Transformer-based models on molecular graphs that existing works have ignored. (1) As only atoms are abstracted as tokens and then reconstructed, the chemical bonds are not decided in the decoded molecule, making molecules with different arrangements of the same atoms indistinguishable. (2) Although a high mask ratio that corresponds to a challenging reconstruction task has been proved beneficial in the vision domain, it cannot be trivially leveraged on molecular graphs as there is less redundancy of information in graph data. To resolve these issues, we propose a novel framework, Molecular Graph Mask AutoEncoder (MGMAE). As the first step in MGMAE, we transform each molecular graph into a heterogeneous atom-bond graph to fully use the bond attributes and design unidirectional position encoding for such graphs. Then we propose a hybrid masking mechanism that exploits the complementary nature between atoms' attributive and spatial features. Meanwhile, we compensate for the mask embedding by a dynamic aggregation representation that exploits the correlations between topologically adjacent tokens. As a result, MGMAE can reconstruct the masked atoms, the masked bonds, and the relative distance among atoms simultaneously, with a high mask ratio. We compare MGMAE with the state-of-the-art methods on various molecular benchmarks and show the competitiveness of MGMAE in both regression and classification tasks.

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning tickets requires iterative training and pruning, which is particularly expensive for finding meta winning tickets. To this end, then we investigate the inter- and intra-layer patterns among different meta winning tickets, and propose a scheme for early detection of a meta winning ticket. The proposed scheme enables efficient training in resource-limited devices. Besides, it also designs a lightweight solution to search the meta winning ticket. Evaluations on standard few-shot classification benchmarks show that we can find competitive meta winning tickets with 20% weights of the original backbone, while incurring only 8%-14% (Conv-4) and 19%-29% (ResNet-12) computation overhead (measured by FLOPs) of the standard winning ticket finding scheme.

The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications. However, federated graph learning (FGL), even though graph data are prevalent, has not been well supported due to its unique characteristics and requirements. The lack of FGL-related framework increases the efforts for accomplishing reproducible research and deploying in real-world applications. Motivated by such strong demand, in this paper, we first discuss the challenges in creating an easy-to-use FGL package and accordingly present our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo and ModelZoo for out-of-the-box FGL capability; (3) an efficient model auto-tuning component; and (4) off-the-shelf privacy attack and defense abilities. We validate the effectiveness of FS-G by conducting extensive experiments, which simultaneously gains many valuable insights about FGL for the community. Moreover, we employ FS-G to serve the FGL application in real-world E-commerce scenarios, where the attained improvements indicate great potential business benefits. We publicly release FS-G, as submodules of FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's research and enable broad applications that would otherwise be infeasible due to the lack of a dedicated package.

Recently, Neural Architecture Search (NAS) for GNN has received increasing popularity as it can seek an optimal architecture for a given new graph. However, the optimal architecture is applied to all the instances (i.e., nodes, in the context of graph) equally, which might be insufficient to handle the diverse local patterns ingrained in a graph, as shown in this paper and some very recent studies. Thus, we argue the necessity of node-wise architecture search for GNN. Nevertheless, node-wise architecture cannot be realized by trivially applying NAS methods node by node due to the scalability issue and the need for determining test nodes' architectures. To tackle these challenges, we propose a framework wherein the parametric controllers decide the GNN architecture for each node based on its local patterns. We instantiate our framework with depth, aggregator and resolution controllers, and then elaborate on learning the backbone GNN model and the controllers to encourage their cooperation. Empirically, we justify the effects of node-wise architecture through the performance improvements introduced by the three controllers, respectively. Moreover, our proposed framework significantly outperforms state-of-the-art methods on five of the ten real-world datasets, where the diversity of these datasets has hindered any graph convolution-based method to lead on them simultaneously. This result further confirms that node-wise architecture can help GNNs become versatile models.

Although neural networks have achieved great successes in various machine learning tasks, people can hardly know what neural networks learn from data due to their black-box nature. The lack of such explainability is one of the limitations of neural networks when applied in domains, e.g., healthcare and finance, that demand transparency and accountability. Moreover, explainability is beneficial for guiding a neural network to learn the causal patterns that can extrapolate out-of-distribution (OOD) data, which is critical in real-world applications and has surged as a hot research topic.

User-User interaction recommendation, or interaction recommendation, is an indispensable service in social platforms, where the system automatically predicts with whom a user wants to interact. In real-world social platforms, we observe that user interactions may occur in diverse scenarios, and new scenarios constantly emerge, such as new games or sales promotions. There are two challenges in these emerging scenarios: (1) The behavior of users on the emerging scenarios could be different from existing ones due to the diversity among scenarios; (2) Emerging scenarios may only have scarce user behavioral data for model learning. Towards these two challenges, we present KoMen, a Domain Knowledge Guided Meta-learning framework for Interaction Recommendation. KoMen first learns a set of global model parameters shared among all scenarios and then quickly adapts the parameters for an emerging scenario based on its similarities with the existing ones. There are two highlights of KoMen: (1) KoMen customizes global model parameters by incorporating domain knowledge of the scenarios (e.g., a taxonomy that organizes scenarios by their purposes and functions), which captures scenario inter-dependencies with very limited training. (2) KoMen learns the scenario-specific parameters through a mixture-of-expert architecture, which reduces model variance resulting from data scarcity while still achieving the expressiveness to handle diverse scenarios. Extensive experiments demonstrate that KoMen achieves state-of-the-art performance on a public benchmark dataset and a large-scale real industry dataset. Remarkably, KoMen improves over the best baseline w.r.t. weighted ROC-AUC by 2.14% and 2.03% on the two datasets, respectively. Our code is available at: https://github.com/Veronicium/koMen.

Machine Learning methods have been adopted for a wide range of real-world applications, ranging from social networks, online image/video-sharing platforms, and e-commerce to education, healthcare, etc. However, in practice, a large amount of effort is required to tune several components of machine learning methods, including data representation, hyperparameter, and model architecture, in order to achieve a good performance. To alleviate the required tunning efforts, Automated Machine Learning (AutoML), which can automate the process of applying machine learning methods, has been studied in both academy and industry recently. In this tutorial, we will introduce the main research topics of AutoML, including Hyperparameter Optimization, Neural Architecture Search, and Meta-Learning. Two emerging topics of AutoML, Automatic Feature Generation and Machine Learning Guided Database, will also be discussed since they are important components for real-world applications. For each topic, we will motivate it with application examples from industry, illustrate the state-of-the-art methodologies, and discuss some future research directions based on our experience from industry and the trends in academy.

Machine learning methods have been adopted for various real-world applications, ranging from social networks, online image/video-sharing platforms, and e-commerce to education, healthcare, etc. However, several components of machine learning methods, including data representation, hyperparameter and model architecture, can largely affect their performance in practice. Moreover, the explosions of data scale and model size make the optimization of these components more and more time-consuming for machine learning developers. To tackle these challenges, Automated Machine Learning (AutoML) aims to automate the process of applying machine learning methods to solve real-world application tasks, reducing the time of tuning machine learning methods while maintaining good performance. In this tutorial, we will introduce the main research topics of AutoML, including Hyperparameter Optimization, Neural Architecture Search and Meta-Learning. Two emerging topics of AutoML, DNN-based Feature Generation and Machine Learning Guided Database, will also be discussed as they are important components for real-world applications. For each topic, we will motivate it with examples from industry, illustrate the state-of-the-art methods, and discuss their pros and cons from both perspectives of industry and academy. We will also discuss some future research directions based on our experience from industry and the trends in academy.

High-order interactive features capture the correlation between different columns and thus are promising to enhance various learning tasks on ubiquitous tabular data. To automate the generation of interactive features, existing works either explicitly traverse the feature space or implicitly express the interactions via intermediate activations of some designed models. These two kinds of methods show that there is essentially a trade-off between feature interpretability and search efficiency. To possess both of their merits, we propose a novel method named Feature Interaction Via Edge Search (FIVES), which formulates the task of interactive feature generation as searching for edges on the defined feature graph. Specifically, we first present our theoretical evidence that motivates us to search for useful interactive features with increasing order. Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph. In this way, the proposed FIVES method simplifies the time-consuming traversal as a typical training course of GNN and enables explicit feature generation according to the learned adjacency tensor. Experimental results on both benchmark and real-world datasets show the advantages of FIVES over several state-of-the-art methods. Moreover, the interactive features identified by FIVES are deployed on the recommender system of Taobao, a worldwide leading e-commerce platform. Results of an online A/B testing further verify the effectiveness of the proposed method FIVES, and we further provide FIVES as AI utilities for the customers of Alibaba Cloud.

Online shopping has been a habit of more and more people, while most users are unable to craft an informative query, and thus it often takes a long search session to satisfy their purchase intents. We present AliISA - a shopping assistant which offers users some tips to further specify their queries during a search session. With such an interactive search, users tend to find targeted items with fewer page requests, which often means a better user experience. Currently, AliISA assists tens of millions of users per day, earns more usage than existing systems, and consequently brings in a 5% improvement in CVR. In this paper, we present our system, describe the underlying techniques, and discuss our experience in stabilizing reinforcement learning under an E-commerce environment.