Dynamic Graph Learning

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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Psychologically-Grounded Graph Modeling for Interpretable Depression Detection
Apr 27, 2026Rishitej Reddy Vyalla, Kritarth Prasad, Avinash Anand +4Depression Detection Dynamic Graph Learning
IIIT Delhi, New Delhi, India · Singapore Institute of Technology, Singapore · Nanyang Technological University, Singapore +3
GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA
Apr 23, 2026Anvitha Ramachandran, Dhruv Parikh, Viktor PrasannaEfficient Neural Network Inference Dynamic Graph Learning
University of Southern California, Los Angeles, California, USA
Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning
Apr 20, 2026Mert Can Turkmen, Eng Leong Tan, Yee Hui LeeProbabilistic Forecasting Dynamic Graph Learning
Nanyang Technological University Singapore
SkillGraph: Self-Evolving Multi-Agent Collaboration with Multimodal Graph Topology
Apr 19, 2026Zheng Nie, Ruolin Shen, Xinlei Yu +3Multi-Agent Collaboration Dynamic Graph Learning
National University of Singapore, Singapore · Technical University of Munich, Munich, Germany · Zhejiang University, China
Learning Ad Hoc Network Dynamics via Graph-Structured World Models
Apr 16, 2026Can Karacelebi, Yusuf Talha Sahin, Elif Surer +1World Model Learning World Models
Department of Computer Engineering, Middle East Technical University, Ankara, Turkiye · Türk Telekom, Ankara, Turkiye · Graduate School of Informatics, Middle East Technical University, Ankara, Turkiye
DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs
Feb 26, 2026Tyler Bonnet, Marek ReiGraph Representation Learning Self-Supervised Pre-Training
Imperial College London
A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction
Feb 15, 2026Nafiseh Sadat Sajadi, Behnam Bahrak, Mahdi Jafari SiavoshaniTemporal GNNs Link Prediction
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. · Tehran Institute for Advanced Studies, Khatam University, Tehran, Iran.
What Do Temporal Graph Learning Models Learn?
Oct 10, 2025Abigail J. Hayes, Tobias Schumacher, Markus StrohmaierTemporal GNNs Temporal Link Prediction
University of Mannheim · RWTH Aachen University · GESIS - Leibniz Institute for the Social Sciences +1
Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
Aug 20, 2025Jiafeng Xiong, Rizos SakellariouGraph Structure Learning Temporal GNNs
Department of Computer Science, University of Manchester, UK
Efficient Traffic State Prediction With Dynamic Joint Spatio-Temporal Relation Inference
Apr 10, 2025Zhifeng Hao, Kai Hu, Juncai Zhang +2Temporal Convolutional Networks Dynamic Graph Learning
School of Cyberspace Security, Hainan University, Haikou, China
DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
Apr 2, 2025Xiaobei Zou, Luolin Xiong, Kexuan Zhang +2Non-Stationary Time Series Forecasting Dynamic Graph Learning
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China · Faculty of Informatics, Università della Svizzera italiana, 69000 Lugano, Switzerland · Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Information propagation dynamics in Deep Graph Networks
Oct 14, 2024Alessio GravinaGraph Neural Networks Graph Representation Learning
University of Pisa Department of Computer Science
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
May 29, 2024Lanting Fang, Yulian Yang, Yawei Zhang +3Causal Representation Learning Structural Causal Models
1Beijing Institue of Technology · Southeast University · Wuhan University
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
Jan 10, 2020Haruki Yokota, Koki Yamada, Yuichi Tanaka +1Graph Signal Processing Graph Structure Learning
Graduate School of Engineering, The University of Osaka, Osaka 565-0871, Japan · Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan · Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089 USA

Apr 27, 2026, cs.CLJ/K move · Enter open · S save

Psychologically-Grounded Graph Modeling for Interpretable Depression Detection

Rishitej Reddy Vyalla, Kritarth Prasad, Avinash Anand, Erik Cambria+3

IIIT Delhi, New Delhi, India · Singapore Institute of Technology, Singapore · Nanyang Technological University, Singapore+3

Depression Detection Dynamic Graph Learning Causal Interpretability

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Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data scarcity and a lack of clinical interpretability. Existing approaches typically rely on black-box deep learning architectures that struggle to model the subtle, temporal evolution of depressive symptoms or account for participant-specific heterogeneity. In this work, we propose PsyGAT (Psychological Graph Attention Network), a psychologically grounded framework that models conversational sessions as dynamic temporal graphs. We introduce Psychological Expression Units (PEUs) to explicitly encode utterance-level clinical evidence, structuring the session graph to capture transitions in psychological states rather than mere semantic dependencies. To address the critical class imbalance in depression datasets, we employ clinically approved persona-based data augmentation, enable robust model learning. Additionally, we integrate session-level personality context directly into the graph structure to disentangle trait-based behavior from acute depressive symptoms. PsyGAT achieves state-of-the-art performance, surpassing both strong graph-based baselines and closed-source LLMs like GPT-5, achieving 89.99 and 71.37 Macro F1 scores in DAIC-WoZ and E-DAIC, respectively. We further introduce Causal-PsyGAT, an interpretability module that identifies symptom triggers. Experiments show a 20% improvement in MRR for identifying causal indicators, effectively bridging the gap between depression monitoring and clinical explainability. The full augmented dataset is publicly available at https://doi.org/10.6084/m9.figshare.31801921.

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