README.md

README.md

@@ -29,7 +29,7 @@ Publications within each conference and year below are organised into topic-spec
 - ## Data Mining Conferences
    * ### [KDD](https://kdd2025.kdd.org/) - [2024](https://github.com/naganandy/graph-based-deep-learning-literature/tree/master/conference-publications/folders/years/2024/publications_kdd24/README.md) | [2023](https://github.com/naganandy/graph-based-deep-learning-literature/tree/master/conference-publications/folders/years/2023/publications_kdd23/README.md) | [2022](https://github.com/naganandy/graph-based-deep-learning-literature/tree/master/conference-publications/folders/years/2022/publications_kdd22/README.md) | [2021](https://github.com/naganandy/graph-based-deep-learning-literature/tree/master/conference-publications/folders/years/2021/publications_kdd21/README.md) | [2020](https://github.com/naganandy/graph-based-deep-learning-literature/tree/master/conference-publications/folders/years/2020/publications_kdd20/README.md) | [2019](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2019/publications_kdd19/README.md) | [2018](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2018/README.MD#kdd-2018-aug)
    * ### [ICDM](https://icdm2024.org/) - [2024](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2024/publications_icdm24/README.md) | [2023](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2023/publications_icdm23/README.md) | [2022](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2022/publications_icdm22/README.md) | [2021](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2021/publications_icdm21/README.md) | [2020](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2020/publications_icdm20/README.md) | [2019](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2019/README.MD#icdm-2019-nov) | [2018](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2018/README.MD#icdm-2018-nov)
-   * ### [WSDM](https://www.wsdm-conference.org/2025/) - [2024](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2024/publications_wsdm24/README.md) | [2023](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2023/publications_wsdm23/README.md) | [2022](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2022/publications_wsdm22/README.md) | [2021](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2021/publications_wsdm21/README.md) | [2020](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2020/README.MD#wsdm-2020-feb) | [2019](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2019/README.MD#wsdm-2019-jan) 
+   * ### [WSDM](https://www.wsdm-conference.org/2025/) - [2025](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2025/publications_wsdm25/README.md) | [2024](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2024/publications_wsdm24/README.md) | [2023](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2023/publications_wsdm23/README.md) | [2022](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2022/publications_wsdm22/README.md) | [2021](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2021/publications_wsdm21/README.md) | [2020](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2020/README.MD#wsdm-2020-feb) | [2019](https://github.com/naganandy/graph-based-deep-learning-literature/blob/master/conference-publications/folders/years/2019/README.MD#wsdm-2019-jan) 
 
 <br> </br>
 

conference-publications/folders/years/2025/publications_wsdm25/README.md

+# [Submissions in WSDM 2025](https://www.wsdm-conference.org/2025/accepted-papers/)
+
+
+
+# Limited Supervision
+- Training MLPs on Graphs without Supervision
+- Inductive Graph Few-shot Class Incremental Learning
+
+
+
+# Link Prediction
+- Hyperdimensional Representation Learning for Node Classification and Link Prediction
+- Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs
+
+
+
+# Language Models
+- LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
+- MoKGNN: Boosting Graph Neural Networks via Mixture of Generic and Task-Specific Language Models
+- UniGLM: Training One Unified Language Model for Text-Attributed Graphs Embedding
+
+
+
+# Dynamic Graphs
+- Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding
+- Hawkes Point Process-Enhanced Dynamic Graph Neural Networks
+
+
+
+# Multigraphs
+- Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection
+- Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity
+
+
+
+# Knowledge Graphs
+- Adaptive Graph Enhancement for Imbalanced Multi-relation Graph Learning
+- Neo-TKGC: Enhancing Temporal Knowledge Graph Completion with Integrated Node Weights and Future Information
+
+
+
+# Heterogeneous Graphs
+- HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning
+- Heterogeneous Graph Diffusion Model
+
+
+
+# Hypergraphs
+- Self-supervised Time-aware Heterogeneous Hypergraph Learning for Dynamic Graph-level Classification
+- An aspect performance-aware hypergraph neural network for review-based recommendation
+
+
+
+# Recommendation
+- Simple Graph Neural Networks for Recommendation
+- Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multi-modal Recommendation
+
+
+
+# Causality
+- Heterophilic Graph Neural Networks Optimization with Causal Message-passing
+- Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data
+
+
+
+# Imbalanced Data
+- Edge Classification on Graphs: New Directions in Topological Imbalance
+- Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
+
+
+
+# Miscellaneous
+- Beyond Message-Passing: Generalization of Graph Neural Networks via Feature Perturbation
+- FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning
+- Q-DISCO: Query-Centric Densest Subgraphs in Networks with Opinion Information
+- Robustness Verification of Deep Graph Neural Networks Tightened by Linear Approximation
+- CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders
+- RSM: Reinforced Subgraph Matching Framework with Fine-grained Operation based Search Plan
+- Improving CTR Prediction with Graph-Enhanced Interest Networks for Sparse Behavior Sequences
+- D$^2$: Customizing Two-Stage Graph Neural Networks for Early Rumor Detection through Cascade Diffusion Prediction