Graph Neural Networks (GNNs) have emerged as powerful tools for various graph mining tasks, yet existing scalable solutions often struggle to balance execution efficiency with prediction accuracy. These difficulties stem from iterative message-passing techniques, which place significant computational demands and require extensive GPU memory, particularly when dealing with the neighbor explosion issue inherent in large-scale graphs. This paper introduces a scalable, low-cost, flexible, and efficient GNN framework called LPS-GNN, which can perform representation learning on 100 billion graphs with a single GPU in 10 hours and shows a 13.8% improvement in User Acquisition scenarios. We examine existing graph partitioning methods and design a superior graph partition algorithm named LPMetis. In particular, LPMetis outperforms current state-of-the-art (SOTA) approaches on various evaluation metrics. In addition, our paper proposes a subgraph augmentation strategy to enhance the model's predictive performance. It exhibits excellent compatibility, allowing the entire framework to accommodate various GNN algorithms. Successfully deployed on the Tencent platform, LPS-GNN has been tested on public and real-world datasets, achieving performance lifts of 8. 24% to 13. 89% over SOTA models in online applications.

Heterogeneous graph neural networks (HGNNs) are effective for modeling multi-relational structured data. Existing HGNNs usually assume the training samples are relatively sufficient, thus focusing on improving the predictive performance by complicating the model architecture with more learnable parameters. In this paper, we instead explore how to design HGNNs when training labels are scarce, under which we observe that existing HGNNs suffer from serious overfitting issues. Inspired by the graph random neural network (GRAND)-a consistency regularization framework for graph learning, we propose a simple yet efficient R-GRAND framework to overcome the issues above. R-GRAND is a general relation-aware consistency regularized training method with both labeled and unlabeled nodes to facilitate the model's generalization capability. It designs a lightweight relational graph convolution neural network (SRGC) as the backbone model to deal with the heterogeneous information. To enable regularized training, we further advance the data augmentation methods of GRAND with a Multi-block DropEdge strategy. The proposed training framework not only excels with its default SRGC backbone but also effectively enhances the performance of other HGNN architectures, such as RGCN and Simple-HGN. Extensive experiments on seven heterogeneous graph datasets demonstrate that R-GRAND can achieve remarkable performance improvements over state-of-theart HGNNs with better generalization ability and high efficiency.

Recently, molecular data mining has attracted a lot of attention owing to its great application potential in material and drug discovery. However, this mining task faces a challenge posed by the scarcity of labeled molecular graphs. To overcome this challenge, we introduce a novel data augmentation and a semi-supervised confidence-aware consistency regularization training framework for molecular property prediction. The core of our framework is a data augmentation strategy on molecular graphs, named DropConn (Dropout Connection). DropConn generates pseudo molecular graphs by softening the hard connections of chemical bonds (as edges), where the soft weights are calculated from edge features so that the adaptive interactions between different atoms can be incorporated. Besides, to enhance the model's generalization ability, a consistency regularization training strategy is proposed to take full advantage of massive unlabeled data. Furthermore, DropConn can serve as a plugin that can be seamlessly added to many existing models. Extensive experiments under both non-pre-training setting and fine-tuning setting demonstrate that DropConn can obtain superior performance (up to 8.22%) over state-of-the-art methods on molecular property prediction tasks. The code is available athttps://github.com/THUDM/DropConn.

Modeling the dynamics into graph neural networks (GNNs) contributes to the understanding of evolution in dynamic graphs, which helps optimize temporal-spatial representations for real-world dynamic network problems. Empirically, dynamic GNN embedding requires additional temporal encoders, which inevitably introduces additional learning parameters to make dynamic GNNs oversized and inefficient. Furthermore, previous dynamic GNN models are under the same fixed temporal term, which causes the short-temporal optimum. To address these issues, we propose the WinGNN framework to model dynamic graphs, which is realized by a simple GNN model with the meta-learning strategy and a novel mechanism of random gradient aggregation. WinGNN calculates the frame-wise loss of the current snapshot and passes the loss gradient to the next to model graph dynamics without temporal encoders. Then it introduces the randomized sliding-window to acquire the window-aware gradienton consecutive snapshots, and the calculated two types of gradient are aggregated to update the GNN, thereby reducing the parameter size and improving the robustness. Experiments on six public datasets show the advantage of our WinGNN compared with existing baselines, where it has reached the optimum in twenty-two out of twenty-four performance metrics.

In recent years, graph neural networks (GNNs) have made great progress in recommendation. The core mechanism of GNNs-based recommender system is to iteratively aggregate neighboring information on the user-item interaction graph. However, existing GNNs treat users and items equally and cannot distinguish diverse local patterns of each node, which makes them suboptimal in the recommendation scenario. To resolve this challenge, we present a node-wise adaptive graph neural network framework ApeGNN. ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree). We perform experiments on six widely-used recommendation datasets. The experimental results show that the proposed ApeGNN is superior to the most advanced GNN-based recommender methods (up to 48.94%), demonstrating the effectiveness of node-wise adaptive aggregation.

Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem of concept recommendation in this paper, which can be viewed as recommending knowledge to users in a fine-grained way. We put forward a novel approach, termedHinCRec-RL, forConceptRecommendation in MOOCs, which is based onHeterogeneousInformationNetworks andReinforcementLearning. In particular, we propose to shape the problem of concept recommendation within a reinforcement learning framework to characterize the dynamic interaction between users and knowledge concepts in MOOCs. Furthermore, we propose to form the interactions among users, courses, videos, and concepts into aheterogeneous information network (HIN)to learn the semantic user representations better. We then employ an attentional graph neural network to represent the users in the HIN, based on meta-paths. Extensive experiments are conducted on a real-world dataset collected from a Chinese MOOC platform,XuetangX, to validate the efficacy of our proposed HinCRec-RL. Experimental results and analysis demonstrate that our proposed HinCRec-RL performs well when compared with several state-of-the-art models.

Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-of-the-art performance for this problem. However, it is difficult for GRAND to handle large-scale graphs since its effectiveness relies on computationally expensive data augmentation procedures. In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning. To address the above issue, we develop a generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general propagation matrix and employ it to perform graph data augmentation in a mini-batch manner. We show that both the low time and space complexities of GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we introduce a confidence-aware consistency loss into the model optimization of GRAND+, facilitating GRAND+’s generalization superiority. We conduct extensive experiments on seven public datasets of different sizes. The results demonstrate that GRAND+ 1) is able to scale to large graphs and costs less running time than existing scalable GNNs, and 2) can offer consistent accuracy improvements over both full-batch and scalable GNNs across all datasets.

The prosperity of massive open online courses provides fodder for plentiful research efforts on adaptive learning. However, current open-access educational datasets are still far from sufficient to meet the need for various topics of adaptive learning. Existing released datasets often cover only small-scale data, lack fine-grained knowledge concepts. They are even difficult to curate and supplement due to platform limitations. In this work, we construct MOOCCubeX, a large, knowledge-centered repository consisting of 4,216 courses, 230,263 videos, 358,265 exercises, 637,572 fine-grained concepts and over 296 million behavioral data of 3,330,294 students, for supporting the research topics on adaptive learning in MOOCs. Licensed by XuetangX, one of the largest MOOC websites in China, we obtain abundant and diverse course resources and student behavioral data and are permitted to make subsequent periodic updates. We propose a framework to accomplish data processing, weakly supervised fine-grained concept graph mining, and data curation to improve usability and richness. Based on the fine-grained concepts, we re-organize the data from the knowledge perspective and acquire more external learning resources from the web. Our repository is now available at https://github.com/THU-KEG/MOOCCubeX.

Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the implicit feedback, but negative sampling in GNN-based CF has been largely unexplored. In this work, we propose to study negative sampling by leveraging both the user-item graph structure and GNNs' aggregation process. We present the MixGCF method---a general negative sampling plugin that can be directly used to train GNN-based recommender systems. In MixGCF, rather than sampling raw negatives from data, we design the hop mixing technique to synthesize hard negatives. Specifically, the idea of hop mixing is to generate the synthetic negative by aggregating embeddings from different layers of raw negatives' neighborhoods. The layer and neighborhood selection process are optimized by a theoretically-backed hard selection strategy. Extensive experiments demonstrate that by using MixGCF, state-of-the-art GNN-based recommendation models can be consistently and significantly improved, e.g., 26% for NGCF and 22% for LightGCN in terms of [email protected]

Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements. In this work, we present a systematical reproduction of 12 recent HGNNs by using their official codes, datasets, settings, and hyperparameters, revealing surprising findings about the progress of HGNNs. We find that the simple homogeneous GNNs, e.g., GCN and GAT, are largely underestimated due to improper settings. GAT with proper inputs can generally match or outperform all existing HGNNs across various scenarios. To facilitate robust and reproducible HGNN research, we construct the Heterogeneous Graph Benchmark (HGB) , consisting of 11 diverse datasets with three tasks. HGB standardizes the process of heterogeneous graph data splits, feature processing, and performance evaluation. Finally, we introduce a simple but very strong baseline Simple-HGN-which significantly outperforms all previous models on HGB-to accelerate the advancement of HGNNs in the future.

Massive open online courses (MOOCs) are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attract students' interest, the recommendation system is applied by MOOCs providers to recommend courses to students. However, as a course usually consists of a number of video lectures, with each one covering some specific knowledge concepts, directly recommending courses overlook students' interest to some specific knowledge concepts. To fill this gap, in this paper, we study the problem of knowledge concept recommendation. We propose an end-to-end graph neural network based approach calledAttentional Heterogeneous Graph Convolutional Deep Knowledge Recommender (ACKRec) for knowledge concept recommendation in MOOCs. Like other recommendation problems, it suffers from sparsity issue. To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network. In addition to students and knowledge concepts, we consider other types of entities (e.g., courses, videos, teachers) and construct a heterogeneous information network (HIN) to capture the corresponding fruitful semantic relationships among different types of entities and incorporate them into the representation learning process. Specifically, we use meta-path on the HIN to guide the propagation of students' preferences. With the help of these meta-paths, the students' preference distribution with respect to a candidate knowledge concept can be captured. Furthermore, we propose an attention mechanism to adaptively fuse the context information from different meta-paths, in order to capture the different interests of different students. To learn the parameters of the proposed model, we propose to utilize extended matrix factorization (MF). A series of experiments are conducted, demonstrating the effectiveness of ACKRec across multiple popular metrics compared with state-of-the-art baseline methods. The promising results show that the proposed ACKRec is able to effectively recommend knowledge concepts to students pursuing online learning in MOOCs.