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- Adversarial Attack and Defense on Graph Data: A Survey409
This review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models.
- Heterogeneous Network Representation Learning: A Unified Framework With Survey and Benchmark324
This work aims to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), and provides a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
- Bridging Collaborative Filtering and Semi-Supervised Learning316
This work proposes to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs.
- Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey311
A systematic taxonomy is proposed that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other.
- BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks244
This work presents BrainGB, a benchmark for brain network analysis with GNNs, a standardizes the process by summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and modularizing the implementation of GNN designs.
- FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation161
FBNETGEN is developed, a task-aware and interpretable fMRI analysis framework via deep brain network generation which learns to transform raw time-series features into task-oriented brain networks and provides unique interpretations by highlighting prediction-related brain regions.
- GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction154
The proposed \proj is a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection, and excels at detecting all three types of anomalies across the datasets, demonstrating its comprehensive anomaly detection capabilities.
- GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning146
This paper proposes GuardAgent, the first LLM agent as a guardrail to other LLM agents, and proposes two novel benchmarks: an EICU-AC benchmark for assessing privacy-related access control for healthcare agents and a Mind2Web-SC benchmark for safety evaluation for web agents.
- EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records127
This work proposes EHRAgent, a large language model (LLM) agent empowered with accumulative domain knowledge and robust coding capability that enables autonomous code generation and execution to facilitate clinicians in directly interacting with EHRs using natural language.
- On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs116
This paper points out the two types of artificial node features, i.e., positional and structural node features and provides insights on why each of them is more appropriate for certain tasks, thereby leading to a practical guideline on the choices between different artificial nodes features for GNNs on non-attributed graphs.
- Conditional Structure Generation through Graph Variational Generative Adversarial Nets114
This work forms the novel problem of conditional structure generation, and proposes a novel unified model of graph variational generative adversarial nets (CondGen) to handle the intrinsic challenges of flexible context-structure conditioning and permutation-invariant generation.
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- I Know You'll Be Back93
This paper develops ClusChurn, a systematic two-step framework for interpretable new user clustering and churn prediction, based on the intuition that properuser clustering can help understand and predict user churn.
- Understanding Structural Vulnerability in Graph Convolutional Networks78
This work theoretically and empirically demonstrate that structural adversarial examples can be attributed to the non-robust aggregation scheme (i.e., the weighted mean) of GCNs, and takes advantage of the breakdown point which can quantitatively measure the robustness of aggregation schemes.
- 4SDrug: Symptom-based Set-to-set Small and Safe Drug Recommendation77
A novel framework of Symptom-based Set-to-set Small and Safe drug recommendation (4SDrug) is proposed, which outperforms all baselines in most effectiveness measures, while yielding the smallest sets of recommended drugs and 26.83% DDI rate reduction from the ground-truth data.
- When Do GNNs Work: Understanding and Improving Neighborhood Aggregation75
This paper proposes novel metrics that quantitatively measure these two circumstances and integrate them into an Adaptive-layer module and shows that allowing for node-specific aggregation degrees have significant advantage over current GNNs.
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- A review on knowledge graphs for healthcare: Resources, applications, and promises73
HKGs have emerged as a powerful tool for structuring medical knowledge, with broad applications across biomedical research, clinical decision-making, and public health, highlighting the potential of combining knowledge graphs with advanced machine learning models for healthcare transformation.
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- SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction66
This work proposes to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE by assessing model uncertainty, which boosts the performance via evidence-based data augmentation and ensemble inference while reducing the computational cost.
- Structure-Enhanced Heterogeneous Graph Contrastive Learning63
A novel method to generate multiple semantic views for HGs and advocate the explicit use of structure embedding, which enriches the model with local structural patterns of the underlying HGs, so as to better mine true and hard negatives for GCL.
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- Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond60
This work aims to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), and provides a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
- Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis57
This work develops a novel multiview GNN model which takes advantage of the message passing scheme by propagating messages based on degree statistics and brain region connectivities and employs contrastive learning for multimodal fusion.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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