Recently, Graph Foundation Models (GFMs) have emerged as a significant research topic in graph machine learning. Compared with traditional graph neural networks, GFMs demonstrate impressive zero-shot generalization across different domains and tasks through large-scale pre-training on extensive and diverse graph data. Despite the initial success of pre-training, existing GFMs face challenges such as extreme time consumption and the presence of redundancy and noise in pre-training data. To alleviate these issues, we present the first exploration of data-centric GFM, which aims to optimize pre-training data (i.e., a set of subgraphs) to establish a more efficient GFM while maintaining robust performance across various downstream tasks. We propose DCGFM, a plug-and-play approach for Data-Centric GFM that incorporates the idea of data pruning to remove redundant and less informative subgraphs from the pre-training data, thereby improving both efficiency and effectiveness. Specifically, DCGFM consists of two components: (1) a model-agnostic hard pruning module that filters out subgraphs with lower informativity scores by considering both the semantics and structures of subgraphs; and (2) a model-aware soft pruning module that dynamically prunes subgraphs with lower loss values in each pre-training epoch with a gradient rescaling strategy. Extensive experiments on representative GFM backbones demonstrate DCGFM's efficiency and effectiveness. Remarkably, DCGFM achieves even better performance using only 30% of the pre-training data. Codes and data are available at https://github.com/Yuhan1i/DCGFM.

Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness, and trustworthiness. To provide personalized and interpretable explanations, existing works often combine the generation capabilities of large language models (LLMs) with collaborative filtering (CF) information. CF information extracted from the user-item interaction graph captures the user behaviors and preferences, which is crucial for providing informative explanations. However, due to the complexity of graph structure, effectively extracting the CF information from graphs still remains a challenge. Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations. To address these challenges, we propose G-Refer, a framework using Graph Retrieval-augmented large language models (LLMs) for explainable recommendation. Specifically, we first employ a hybrid graph retrieval mechanism to retrieve explicit CF signals from both structural and semantic perspectives. The retrieved CF information is explicitly formulated as human-understandable text by the proposed graph translation and accounts for the explanations generated by LLMs. To bridge the modality gap, we introduce knowledge pruning and retrieval-augmented fine-tuning to enhance the ability of LLMs to process and utilize the retrieved CF information to generate explanations. Extensive experiments show that G-Refer achieves superior performance compared with existing methods in both explainability and stability. Codes and data are available at https://github.com/Yuhan1i/G-Refer.

Optimization algorithms with momentum have been widely used for building deep learning models because of the fast convergence rate. Momentum helps accelerate Stochastic gradient descent in relevant directions in parameter updating, minifying the oscillations of the parameters update route. The gradient of each step in optimization algorithms with momentum is calculated by a part of the training samples, so there exists stochasticity, which may bring errors to parameter updates. In this case, momentum placing the influence of the last step to the current step with a fixed weight is obviously inaccurate, which propagates the error and hinders the correction of the current step. Besides, such a hyperparameter can be extremely hard to tune in applications as well. In this paper, we introduce a novel optimization algorithm, namely, Discriminative wEight on Adaptive Momentum (DEAM). Instead of assigning the momentum term weight with a fixed hyperparameter, DEAM proposes to compute the momentum weight automatically based on the discriminative angle. The momentum term weight will be assigned with an appropriate value that configures momentum in the current step. In this way, DEAM involves fewer hyperparameters. DEAM also contains a novel backtrack term, which restricts redundant updates when the correction of the last step is needed. The backtrack term can effectively adapt the learning rate and achieve the anticipatory update as well. Extensive experiments demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training the deep learning models of both convex and nonconvex situations.

Graph neural networks (GNNs) have shown convincing performance in learning powerful node representations that preserve both node attributes and graph structural information. However, many GNNs encounter problems in effectiveness and efficiency when they are designed with a deeper network structure or handle large-sized graphs. Several sampling algorithms have been proposed for improving and accelerating the training of GNNs, yet they ignore understanding the source of GNNs performance gain. The measurement of information within graph data can help the sampling algorithms to keep high-value information while removing redundant information and even noise. In this paper, we propose a Metric-Guided (MeGuide) subgraph learning framework for GNNs. MeGuide employs two novel metrics: Feature Smoothness and Connection Failure Distance to guide the subgraph sampling and mini-batch based training. Feature Smoothness is designed for analyzing the feature of nodes to retain the most valuable information, while Connection Failure Distance can measure the structural information to control the size of subgraphs. We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple data sets.

Fraud detection is extremely critical for e-commerce business platforms. Utilizing graph structure data and identifying unexpected dense subgraphs as suspicious is a category of commonly used fraud detection methods. Among them, spectral methods solve the problem efficiently but hurt the performance due to the relaxed constraints. Heuristic methods cannot be accelerated with parallel computation and fail to control the scope of returned suspicious nodes. These drawbacks affect the real-world applications of existing graph-based methods. In this paper, we propose an Ensemble based Fraud DETection (ENSEMFDET) method to scale up fraud detection in bipartite graphs. By oversampling the graph and solving the subproblems, the ensemble approach further votes suspicious nodes without sacrificing the prediction accuracy. Extensive experiments have been done on real transaction data from JD.com and demonstrate the effectiveness, practicability, and scalability of ENSEMFDET.

Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training instances, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter (Active Iterative Alignment), is introduced in this paper. The model ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments were performed on a real-world aligned networks dataset, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.

Optimization algorithms with momentum, e.g., (ADAM) helps accelerate SGD in parameter updating, which can minify the oscillations of parameters update route. However, the fixed momentum weight (e.g., β1in ADAM) will propagate errors in momentum computing. Besides, such a hyperparameter can be extremely hard to tune in applications. In this paper, we introduce a novel optimization algorithm, namely Discriminative wEight on Adaptive Momentum (DEAM). DEAM proposes to compute the momentum weight automatically based on the discriminative angle. The momentum term weight will be assigned with an appropriate value which configures the influence of momentum in the current step. In addition, DEAM also contains a novel backtrack term, which restricts redundant updates when the correction of the last step is needed. The backtrack term can effectively adapt the learning rate and achieve the anticipatory update as well. Extensive experiments demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training various models. A full version of this paper can be accessed in [1].

The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media does not exist independently in the form of an article. Many other entities, such as news creators, news subjects, and so on, exist on social media and have relationships with news articles. Different entities and relationships can be modeled as a heterogeneous information network (HIN). In this paper, we attempt to solve the fake news detection problem with the support of a news-oriented HIN. We propose a novel fake news detection framework, namely Adversarial Active Learning-based Heterogeneous Graph Neural Network (AA-HGNN) which employs a novel hierarchical attention mechanism to perform node representation learning in the HIN. AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data. An adversarial selector will be trained to query high-value candidates for the active learning framework. When the adversarial active learning is completed, AA-HGNN detects fake news by classifying news article nodes. Experiments with two real-world fake news datasets show that our model can outperform text-based models and other graph-based models when using less labeled data benefiting from the adversarial active learning. As a model with generalizability, AA-HGNN also has the ability to be widely used in other node classification-related applications on heterogeneous graphs.

Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training data, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter, is introduced in this paper. ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments are conducted on real-world aligned networks datasets, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.