This paper investigates the task of unsupervised graph domain adaptation, which facilitates the transfer of knowledge from labeled source graphs to unlabeled target graphs. Recent approaches usually utilize graph contrastive learning and pseudo-labeling to learn from unlabeled target data, which could introduce potential biased representations and supervision of target graphs resulting from serious shifts across two domains. Towards this end, we propose a novel framework named Diffusion-guided Unbiased Discriminative Learning (DisCo) for unsupervised graph domain adaptation. The core of our DisCo is to leverage both feature disentanglement and cross-domain diffusion signals to remove the potential biases for target graphs. In particular, we first utilize adversarial feature disentanglement to extract causal features that are orthogonal to domain biases. More importantly, we retrieve the labels of cross-domain source graphs to generate the conditions, which would be utilized to optimize a diffusion model for label denoising. The consistency between pseudo-labels and denoised labels is measured to reduce the potential biases during domain alignment. Extensive experiments on several real-world benchmarks demonstrate that our proposed DisCo consistently outperforms competing state-of-the-art baselines.

This paper investigates a novel and critical problem of Graph Cross-Domain Continual Fine-Tuning, which aims to adapt a large pre-trained Graph Foundation Model across diverse domains. Existing continual graph learning methods are mostly limited to incremental settings within only a single domain, and are typically trained from scratch. As a result, they fail to handle cross-domain shifts effectively, suffer from severe forgetting, and lack transferability. To address these challenges, we present G-CORMoL, Graph Continual Fine-tuning with Orthogonal, Router-driven Mixture of LoRA experts. G-CORMoL achieves effective adaptation while preserving prior knowledge by enforcing mathematical orthogonality between expert LoRA adapters, thereby eliminating interference across tasks. It further supports cross-domain knowledge transfer through a symmetric dual-driven routing mechanism that learns a global composition policy over all learned LoRA experts. In addition, it promotes expert specialization via a contrastive objective with theoretical guarantees. Extensive experiments on different cross-domain task orders demonstrate that G-CORMoL achieves robust state-of-the-art performance, not only preventing catastrophic forgetting but also leveraging accumulated knowledge to enable positive transfer.

Graph-level classification involves analyzing the property of the whole graph, which is typically solved by using graph neural networks (GNNs). Existing efforts generally assume a balanced class distribution. However, real-world data often exhibit long-tailed distributions, i.e., tail classes have significantly fewer samples than head classes, and thus directly applying GNNs is eventually biased toward the head classes, resulting in limited generalization over the tail classes. Moreover, the predictions of existing algorithms are usually not trustworthy, and the trained classifiers remain ignorant to their predictive confidence. Towards this end, in this paper we develop a principled framework called GraphEVER for long-tailed graph-level classification. Technically, GraphEVER incorporates the beliefs of multiple experts and leverages the idea of subjective logic within the Dempster-Shafer Evidence Theory (DST). It can provide the evidence and uncertainty estimation for each expert, where the evidence is parameterized by a Dirichlet distribution to model class probability distribution, and the uncertainty is quantified via a well-defined theoretical framework. In this way, diverse experts can be integrated under DST to endow the classifier with both reliability and robustness. Moreover, we propose an evidence-based routing mechanism to dynamically assign experts, such that the tail classes can receive more attention, while the head classes can reduce redundant engaged experts, further cutting down the computational cost and improving the efficiency. Extensive experiments on seven datasets verify the superiority of our proposed framework.

Homophily, the tendency of similar nodes to connect, is a fundamental phenomenon in network science and a critical factor in the performance of graph neural networks (GNNs). While existing studies primarily explore homophily in homogeneous graphs, where nodes share the same type, real-world networks are often more accurately modeled as heterogeneous graphs (HGs) with diverse node types and intricate cross-type interactions. This structural diversity complicates the analysis of homophily, as traditional homophily metrics fail to account for distinct label spaces across node types. To address this limitation, we introduce the Cross-Type Homophily Ratio (CHR), a novel metric that quantifies homophily based on the similarity of target information across different node types. Additionally, we propose Cross-Type Homophily-guided Graph Editing (CTHGE), a novel method for improving heterogeneous graph neural networks (HGNNs) performance by optimizing cross-type connectivity using Cross-Type Homophily Ratio. Extensive experiments on five HG datasets with nine HGNNs validate the effectiveness of CTHGE, which delivers a maximum relative performance improvement of over 25% for HGNNs on node classification tasks, offering a fresh perspective on cross-type homophily in HGs learning.

Feature Transformation is crucial for classic machine learning that aims to generate feature combinations to enhance the performance of downstream tasks from a data-centric perspective. Current methodologies, such as manual expert-driven processes, iterative-feedback techniques, and exploration-generative tactics, have shown promise in automating such data engineering workflow by minimizing human involvement. However, three challenges remain in those frameworks: (1) It predominantly depends on downstream task performance metrics, as assessment is time-consuming, especially for large datasets. (2) The diversity of feature combinations will hardly be guaranteed after random exploration ends. (3) Rare significant transformations lead to sparse valuable feedback that hinders the learning processes or leads to less effective results. In response to these challenges, we introduce FASTFT, an innovative framework that leverages a trio of advanced strategies. We first decouple the feature transformation evaluation from the outcomes of the generated datasets via the performance predictor. To address the issue of reward sparsity, we developed a method to evaluate the novelty of generated transformation sequences. Incorporating this novelty into the reward function accelerates the model's exploration of effective transformations, thereby improving the search productivity. Additionally, we combine novelty and performance to create a prioritized memory buffer, ensuring that essential experiences are effectively revisited during exploration. Our extensive experimental evaluations validate the performance, efficiency, and traceability of our proposed framework, showcasing its superiority in handling complex feature transformation tasks11The code and data are publicly accessible via Github..

The objective of topic inference in research proposals aims to obtain the most suitable disciplinary division from the discipline system defined by a funding agency. The agency will subsequently find appropriate peer-review experts from their database based on this division. Automated topic inference can reduce human errors caused by manual topic filling, bridge the knowledge gap between funding agencies and project applicants, and improve system efficiency. Existing methods focus on modeling this as a hierarchical multi-label classification problem, using generative models to iteratively infer the most appropriate topic information. However, these methods overlook the gap in scale between interdisciplinary research proposals and non-interdisciplinary ones, leading to an unjust phenomenon where the automated inference system categorizes interdisciplinary proposals as non-interdisciplinary, causing unfairness during the expert assignment. How can we address this data imbalance issue under a complex discipline system and hence resolve this unfairness? In this article, we implement a topic label inference system based on a Transformer encoder–decoder architecture. Furthermore, we utilize interpolation techniques to create a series of pseudo-interdisciplinary proposals from non-interdisciplinary ones during training based on non-parametric indicators, such as cross-topic probabilities and topic occurrence probabilities. This approach aims to reduce the bias of the system during model training. Finally, we conduct extensive experiments on a real-world dataset to verify the effectiveness of the proposed method. The experimental results demonstrate that our training strategy can significantly mitigate the unfairness generated in the topic inference task. To improve the reproducibility of our research, we have released accompanying code by Dropbox. 1

Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations. With the remarkable achievements of node embedding-based Graph Neural Networks (GNNs), we explore the upper bounds of expressiveness inherent to embedding-based methodologies and tackle the challenges by reframing the CF task as a graph-signal processing problem. To this end, we propose PolyCF, a flexible graph signal filter that leverages polynomial graph filters to process interaction signals. PolyCF exhibits the capability to capture spectral features across multiple eigenspaces through a series of Generalized Gram filters and is able to approximate the optimal polynomial response function for recovering missing interactions. A graph optimization objective and a pairwise ranking objective are jointly used to optimize the parameters of the convolution kernel. Experiments on three widely adopted datasets demonstrate the superiority of PolyCF over the state-of-the-art CF methods.

Application programming interface (API) misuse refers to misconceptions or carelessness in the anticipated usage of APIs, threatening the software system’s security. Moreover, API misuses demonstrate significant concealment and are challenging to uncover. Recent advancements have explored enhanced LLMs in a variety of software engineering (SE) activities, such as code repair. Nonetheless, the security implications of using LLMs for these purposes remain underexplored, particularly concerning the issue of API misuse. In this paper, we present an empirical study to observe the bug‐fixing capabilities of LLMs in addressing API misuse related to monitoring resource management (MRM API misuse). Initially, we propose APImisRepair, a real‐world benchmark for repairing MRM API misuse, including buggy programs, corresponding fixed programs, and descriptions of API misuse. Subsequently, we assess the performance of several LLMs using the APImisRepair benchmark. Findings reveal the vulnerabilities of LLMs in repairing MRM API misuse and find several reasons, encompassing factors such as fault localization and a lack of awareness regarding API misuse. Additionally, we have insights on improving LLMs in terms of their ability to fix MRM API misuse and introduce a crafted approach, APImisAP. Experimental results demonstrate that APImisAP exhibits a certain degree of improvement in the security of LLMs.

This article studies the problem of semi-supervised learning on graphs, which aims to incorporate ubiquitous unlabeled knowledge (e.g., graph topology, node attributes) with few-available labeled knowledge (e.g., node class) to alleviate the scarcity issue of supervised information on node classification. While promising results are achieved, existing works for this problem usually suffer from the poor balance of generalization and fitting ability due to the heavy reliance on labels or task-agnostic unsupervised information. To address the challenge, we propose a dual-channel framework for semi-supervised learning on G raphs via K nowledge T ransfer between independent supervised and unsupervised embedding spaces, namely, GKT. Specifically, we devise a dual-channel framework including a supervised model for learning the label probability of nodes and an unsupervised model for extracting information from massive unlabeled graph data. A knowledge transfer head is proposed to bridge the gap between the generalization and fitting capability of the two models. We use the unsupervised information to reconstruct batch-graphs to smooth the label probability distribution on the graphs to improve the generalization of prediction. We also adaptively adjust the reconstructed graphs by encouraging the label-related connections to solidify the fitting ability. Since the optimization of the supervised channel with knowledge transfer contains that of the unsupervised channel as a constraint and vice versa, we then propose a meta-learning-based method to solve the bi-level optimization problem, which avoids the negative transfer and further improves the model’s performance. Finally, extensive experiments validate the effectiveness of our proposed framework by comparing state-of-the-art algorithms.

Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we propose a novel group-wise cascading actor-critic perspective to develop the AI construct of automated feature transformation. Specifically, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Our proposed framework has three technical aims: 1) efficient generation; 2) effective policy learning; 3) accurate state perception. For an efficient generation, we develop a tailored feature clustering algorithm and accelerate generation by feature group-group crossing based generation. For effective policy learning, we propose a cascading actor-critic learning strategy to learn state-passing agents to select candidate feature groups and operations for fast feature generation. Such a strategy can effectively learn policies when the original feature size is large, along with exponentially growing feature generation action space, in which classic Q-value estimation methods fail. For accurate state perception of feature space, we develop a state comprehension method considering not only pointwise feature information but also pairwise feature-feature correlations. Finally, we present extensive experiments and case studies to illustrate 24.7% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.

The peer merit review of research proposals has been the major mechanism to decide grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principle investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs to learn representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-enabled devices and location-based social networks (LBSNs), plays an increasingly important role in recommender systems. It aims to provide users with the convenience to discover their interested places to visit based on previous visits and current status. Most existing methods usually merely leverage recurrent neural networks (RNNs) to explore sequential influences for recommendation. Despite the effectiveness, these methods not only neglect topological geographical influences among POIs, but also fail to model high-order sequential substructures. To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way. KBGNN consists of a geographical module and a sequential module. On the one hand, we construct a geographical graph and leverage a message passing neural network to capture the topological geographical influences. On the other hand, we explore high-order sequential substructures in the user-aware sequential graph using a graph kernel neural network to capture user preferences. Finally, a consistency learning framework is introduced to jointly incorporate geographical and sequential information extracted from two separate graphs. In this way, the two modules effectively exchange knowledge to mutually enhance each other. Extensive experiments conducted on two real-world LBSN datasets demonstrate the superior performance of our proposed method over the state-of-the-arts. Our codes are available at https://github.com/ ang6ang/KBGNN.

To advance the development of science and technology, research proposals are submitted to open-court competitive programs developed by government agencies (e.g., NSF). Proposal classification is one of the most important tasks to achieve effective and fair review assignments. Proposal classification aims to classify a proposal into a length-variant sequence of labels. In this paper, we formulate the proposal classification problem into a hierarchical multi-label classification task. Although there are certain prior studies, proposal classification exhibit unique features: 1) the classification result of a proposal is in a hierarchical discipline structure with different levels of granularity; 2) proposals contain multiple types of documents; 3) domain experts can empirically provide partial labels that can be leveraged to improve task performances. In this paper, we focus on developing a new deep proposal classification framework to jointly model the three features. In particular, to sequentially generate labels, we leverage previously-generated labels to predict the label of next level; to integrate partial labels from experts, we use the embedding of these empirical partial labels to initialize the state of neural networks. Our model can automatically identify the best length of label sequence to stop next label prediction. Finally, we present extensive results to demonstrate that our method can jointly model partial labels, textual information, and semantic dependencies in label sequences, and, thus, achieve advanced performances.

While Graph Neural Network (GNN) has shown superiority in learning node representations of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging problem. The dominating reason is that GNN learns node representations by aggregating neighbors' information regardless of node types. Some work is proposed to alleviate such issue by exploiting relations or meta-path to sample neighbors with distinct categories, then use attention mechanism to learn different importance for different categories. However, one limitation is that the learned representations for different types of nodes should own different feature spaces, while all the above work still project node representations into one feature space. Moreover, after exploring massive heterogeneous graphs, we identify a fact that multiple nodes with the same type always connect to a node with another type, which reveals the many-to-one schema, a.k.a. the hierarchical tree structure. But all the above work cannot preserve such tree structure, since the exact multi-hop path correlation from neighbors to the target node would be erased through aggregation. Therefore, to overcome the limitations of the literature, we propose T-GNN, a tree structure-aware graph neural network model for graph representation learning. Specifically, the proposed T-GNN consists of two modules: (1) the integrated hierarchical aggregation module and (2) the relational metric learning module. The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with gated recurrent unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations. The relational metric learning module aims to preserve the heterogeneity by embedding each type of nodes into a type-specific space with distinct distribution based on similarity metrics. In this way, our proposed T-GNN is capable of simultaneously preserving the heterogeneity and the tree structure inherent in heterogeneous graphs. Finally, we conduct extensive experiments to show the outstanding performance of T-GNN in tasks of node clustering and classification, inductive node clustering and classification, and link prediction.

People share same names in real world. When a digital library user searches for an author name, he may see a mixture of publications by different authors who have the same name. Making distinctions between them is an important prerequisite to improve the quality of services and contents in digital libraries. The general task of author disambiguation is to associate publications which belong to an identical name or names with highly similar spellings to different people entities. In recent years, many researches have been conducted to solve this challenging task. However, some works rely heavily on external knowledge bases and manually annotated data. Some unsupervised learning based works require complex feature engineering. In this paper, we propose a novel and efficient author disambiguation framework which needs no labeled data. We first construct a publication heterogeneous network for each ambiguous name. Then, we use our proposed heterogeneous graph convolutional network embedding method that encodes both graph structure and node attribute information to learn publication representations. After that, we propose a graph enhanced clustering method for name disambiguation that can greatly accelerate the clustering process and need not require the number of distinct persons. Our framework can be continually retrained and applied on incremental disambiguation task when new publications are put in. Experimental results on two datasets show that our framework clearly performs better than several state-of-the-art methods for author disambiguation.