With the widespread adoption of collaborative filtering techniques for personalized recommendations, exposure bias has become a significant challenge.Exposure biasrefers to the tendency of recommendation models to disproportionately favor items with high exposure over those with low exposure. In graph collaborative filtering that uses graph neural networks (GNNs) for recommendations, exposure bias can be exacerbated due to 1) the reliance on positive feedback during graph construction and 2) the effects of the neighbor aggregation step in GNNs. To tackle this challenge, we propose a novel and efficient framework called FUGCF (training-Free andUnbiasedGraphCollaborativeFiltering) to improve both the accuracy and bias mitigation of graph-based personalized recommendations. FUGCF employs a two-stage calculation strategy: it estimates exposure probabilities in the first stage and then leverages them to help derive debiased node embeddings in the second stage. Furthermore, we design a training-free estimation method for FUGCF based on closed-form solutions to enhance its computational efficiency. The extensive experiments on a synthetic dataset and three real-world datasets demonstrate the effectiveness of FUGCF in reducing exposure bias, improving recommendation accuracy, and optimizing computational efficiency.

Graph embedding aims to extract low-dimensional representation vectors, commonly referred to as embeddings, from graph data. The generated embeddings simplify subsequent data analysis and machine learning tasks. Recently, researchers have proposed the use of contrastive learning on graphs to extract node embeddings in an unsupervised manner. Although existing graph contrastive learning methods have significantly advanced this field, there is still potential for further exploration, particularly in optimizing training efficiency and enhancing embedding quality. In this paper, we propose an efficient unsupervised graph embedding method named GEARED. First, the method involves an attributed graph reduction module that converts the raw graph into a reduced graph, greatly improving training efficiency. Second, GEARED employs a dual-level loss with adaptive scaling factors to obtain high-quality embeddings. Experimental results demonstrate that GEARED achieves high classification accuracy, good scalability, and enhanced embedding quality.

The heterogeneous graph, as an effective representation of real-world data, encapsulates rich structural and semantic information. In recent years, numerous Heterogeneous Graph Neural Networks (HGNNs) have been proposed to learn node representations on heterogeneous graphs. Although existing methods have introduced various unique information aggregation and semantic fusion mechanisms, they still exhibit limitations in effectiveness and scalability. In this study, we introduce the gatekeeping theory in heterogeneous graph learning and investigate the primary challenges limiting current HGNNs. To address these challenges, we propose a novel, effective, and scalable heterogeneous graph neural network framework, the Heterogeneous Convolution-oriented Attention Network (HCAN). HCAN enhances the heterogeneous attention mechanism to learn far-sighted weights by encoding long-range relation information into node representation with a convolutional subgraph encoder. To further improve heterogeneous graph representation learning, we propose effective and scalable models based on the HCAN framework. We evaluate HCAN on various commonly used heterogeneous datasets and show that it outperforms the state-of-the-art methods, especially on challenging datasets.

To analyze and process graph data, researchers have proposed Graph Neural Network (GNN) models. In this paper, we focus on methods for boosting the performance of existing GNN models and propose GraphHI, a GNN framework that integrates Hidden Insights to enhance the performance of a given GNN model. We propose to utilize both inter-model and intra-model hidden insights. The inter-model hidden insights encompass the embedding vectors and logit vectors derived from other pretrained models using the same graph data. The intra-model hidden insights incorporate the embedding vectors of other nodes from the same GNN model. To optimize the suitability of hidden insights for GNN model training, we conduct a theoretical analysis of the influence of various forms of the transformed logits and the parameter$T$in the data transformation function. Based on this analysis, a method for setting dynamic personalized parameters in the data transformation is proposed, which is tailored to the current state of each node in the GNN model. To integrate multiple sources of hidden insights, we propose ALC, an algorithm that dynamically sets appropriate combination coefficients for various loss terms. The experimental results show that GraphHI can boost the performance of GNN models using different pretrained models in four different tasks.

Contrastive learning, a powerful self-supervised learning paradigm, has shown its efficacy in learning embed dings from independent and identically distributed (IID) as well as non-IID data without relying on label information. Since high-quality discriminative embeddings form a rich embedding space, which benefits model performance on downstream tasks, it is necessary to study how to improve the quality of contrastive node embeddings in graph contrastive learning. However, there has been limited research on this area. In this paper, we investigate how to generate high-quality contrastive node embeddings based on an in-depth analysis of graph contrastive losses. Firstly, we propose a novel and effective method, GLATE, for estimating the temperatures in three mainstream graph contrastive losses during the training phase. Secondly, we conduct the derivation of GLATE, and the derivation results reveal the specific relationship between the quality of contrastive node embeddings and tem-peratures. Finally, the extensive experiments on 16 benchmark datasets demonstrate that GLATE consistently outperforms the state-of-the-art graph contrastive learning models in terms of both model performance and training efficiency.

Synthetic graph datasets are crucial for the assessment of network analysis algorithms, providing a measure of their effectiveness and efficiency. However, most existing generation techniques typically focus on community formation, neglecting the fact that real-world communities not only emerge but may also dissipate over time. This lifecycle occurs within a finite timeframe, adding complexity to the community dynamics. In this paper, firstly we introduce the concept of time-bound community to characterize communities that are destined to disintegrate. Secondly, we devise GTB, a temporal graph Generation method featuring Time-Bound communities, leveraging newly identified patterns within real datasets that we have collected. Additionally, we propose the advanced Temporal Edge Distribution (TED) model, a key component of G TB, designed for the swift generation of temporal edges within distribution limits and offering a constant time complexity to generate each edge. The TED model not only accommodates diverse distribution configurations but also facilitates its seamless transfer between time-bound communities to enhance time and space efficiency. Finally, extensive experimental results demonstrate that our method outperforms baseline methods by delivering generated content of superior Quality with notably competitive time and space consumption.

Graph embedding aims to extract low-dimensional representation vectors, commonly referred to as embeddings, from graph data. The generated embeddings simplify subsequent data analysis and machine learning tasks. Recently, researchers have proposed the use of contrastive learning on graphs to extract node embeddings in an unsupervised manner. Although existing graph contrastive learning methods have significantly advanced this field, there is still potential for further exploration, particularly in optimizingtraining efficiencyand enhancingembedding quality. In this paper, we propose an efficient unsupervised graph embedding method named GEARED. First, the method involves an attributed graph reduction module that converts the raw graph into a reduced graph, greatly improving model training efficiency. Second, GEARED employs a dual-level loss with adaptive scaling factors to ensure the acquisition of high-quality embeddings. Finally, we conduct a partial derivative analysis to elucidate the specific mechanisms through which GEARED is capable of generating high-quality embeddings. Extensive experimental evaluations on 14 benchmark datasets show that GEARED consistently outperforms state-of-the-art methods in terms of training efficiency and classification accuracy. For instance, GEARED achieves a training speedup of over 40 times on both the CS and Physics datasets while maintaining superior classification accuracy.

Unsupervised graph representation learning, i.e., learning node or graph embeddings from graph data in an unsupervised manner, has become an important problem when we study graph data. With the development of self-supervised learning, researchers have designed graph-level self-supervised learning paradigms and learn embeddings under these paradigms. The learned embeddings can serve as a fine initial solution to downstream tasks such as node classification or graph classification. In this paper, we propose a fast unsupervised graph embedding method, which follows the way of self-supervised learning. This method performs representation learning on the graph under a novel concept called Graph Zoom Learning (abbr. GZL), which is orthogonal to the existing concepts of unsupervised graph embedding, such as random walk and contrastive learning. Two crucial components, graph zoom-out and point-to-point contrast, help GZL reduce the overall training time cost. Specifically, on the one hand, a lightweight miniature graph is generated from the raw graph by graph zoom-out and the learning on the miniature graph is more efficient than the learning on the raw graph; on the other hand, we design the miniature-scale learning on the miniature graph and introduce community structure into this learning pattern, which contributes to the final point-to-point contrast. Since point-to-point contrast is independent of negatives, it makes the whole training more efficient. We conduct extensive experiments to verify the advantage of GZL on representation learning. On two downstream tasks of node classification and graph classification, GZL outperforms the state-of-the-art unsupervised graph embedding methods. Particularly, on the largest experimental graph dataset (ogbn-arxiv) with 169k nodes and 1.1m edges, GZL outperforms the runner-up by 3.3% relative accuracy and achieves up to 22.6x speedup over it.

Recently, learning embeddings of nodes in graphs has attracted increasing research attention. There are two main kinds of graph embedding methods, i.e., transductive embedding methods and inductive embedding methods. The former focuses on directly optimizing the embedding vectors, and the latter tries to learn a mapping function for the given nodes and features. However, little work has focused on applying the learned model from one graph to another, which is a pervasive idea in Computer Vision or Natural Language Processing. Although some of the graph neural networks (GNNs) present a similar motivation, none of them considers graph biases between graphs. In this paper, we present a novel graph embedding problem called Adaptive Task (AT), and propose a unified framework for the adaptive task, which introduces two types of alignment to learn adaptive node embeddings across graphs. Then, based on the proposed framework, a novel Graph Adaptive Embedding network (GraphAE) is designed to address the adaptive task. Furthermore, we extend GraphAE to a multi-graph version to consider a more complex adaptive situation. The extensive experimental results demonstrate that our model significantly outperforms the state-of-the-art methods, and also show that our framework can make a great improvement over a number of existing GNNs.

Many real-world networks have been evolving, and are finely modeled as temporal graphs from the viewpoint of the graph theory. A temporal graph is informative, and always contains two types of information, i.e., the temporal information and topological information, where the temporal information reflects the time when the relationships are established, and the topological information focuses on the structure of the graph. In this paper, we perform time-topology analysis on temporal graphs to extract useful information. Firstly, a new metric named T-cohesiveness is proposed to evaluate the cohesiveness of a temporal subgraph. It defines the cohesiveness of a temporal subgraph from the time and topology dimensions jointly. Specifically, given a temporal graph G s = ( Vs , ε Es ), cohesiveness in the time dimension reflects whether the connections in G s happen in a short period of time, while cohesiveness in the topology dimension indicates whether the vertices in V s are densely connected and have few connections with vertices out of G s . Then, T-cohesiveness is utilized to perform time-topology analysis on temporal graphs, and two time-topology analysis methods are proposed. In detail, T-cohesiveness evolution tracking traces the evolution of the T-cohesiveness of a subgraph, and combo searching finds out all the subgraphs that contain the query vertex and have T-cohesiveness larger than a given threshold. Moreover, a pruning strategy is proposed to improve the efficiency of combo searching. Experimental results confirm the efficiency of the proposed time-topology analysis methods and the pruning strategy.