Saved in:
| Main Authors: | Bose, Kushal, Das, Swagatam |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.13139 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks
by: Bose, Kushal, et al.
Published: (2025)
by: Bose, Kushal, et al.
Published: (2025)
Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration
by: Pratihar, Arghya, et al.
Published: (2025)
by: Pratihar, Arghya, et al.
Published: (2025)
Transformers Are Universally Consistent
by: Ghosh, Sagar, et al.
Published: (2025)
by: Ghosh, Sagar, et al.
Published: (2025)
On the Universal Statistical Consistency of Expansive Hyperbolic Deep Convolutional Neural Networks
by: Ghosh, Sagar, et al.
Published: (2024)
by: Ghosh, Sagar, et al.
Published: (2024)
A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights
by: Pan, Shubhayan, et al.
Published: (2025)
by: Pan, Shubhayan, et al.
Published: (2025)
Consistent Spectral Clustering in Hyperbolic Spaces
by: Ghosh, Sagar, et al.
Published: (2024)
by: Ghosh, Sagar, et al.
Published: (2024)
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
by: Basu, Arkaprabha, et al.
Published: (2024)
by: Basu, Arkaprabha, et al.
Published: (2024)
Grounding the Ungrounded: A Spectral-Graph Framework for Quantifying Hallucinations in Multimodal LLMs
by: Sarkar, Supratik, et al.
Published: (2025)
by: Sarkar, Supratik, et al.
Published: (2025)
A Free Probabilistic Framework for Analyzing the Transformer-based Language Models
by: Das, Swagatam
Published: (2025)
by: Das, Swagatam
Published: (2025)
A Free Probabilistic Framework for Denoising Diffusion Models: Entropy, Transport, and Reverse Processes
by: Das, Swagatam
Published: (2025)
by: Das, Swagatam
Published: (2025)
Self-Tuning Spectral Clustering for Speaker Diarization
by: Raghav, Nikhil, et al.
Published: (2024)
by: Raghav, Nikhil, et al.
Published: (2024)
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning
by: Roy, Srinjoy, et al.
Published: (2024)
by: Roy, Srinjoy, et al.
Published: (2024)
MK-SGC-SC: Multiple Kernel Guided Sparse Graph Construction in Spectral Clustering for Unsupervised Speaker Diarization
by: Raghav, Nikhil, et al.
Published: (2026)
by: Raghav, Nikhil, et al.
Published: (2026)
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
by: Wu, Yuhao, et al.
Published: (2024)
by: Wu, Yuhao, et al.
Published: (2024)
Learning Laplacian Positional Encodings for Heterophilous Graphs
by: Ito, Michael, et al.
Published: (2025)
by: Ito, Michael, et al.
Published: (2025)
Exploring Adaptive Structure Learning for Heterophilic Graphs
by: Kaushik, Garv
Published: (2025)
by: Kaushik, Garv
Published: (2025)
Task-driven Heterophilic Graph Structure Learning
by: Raghuvanshi, Ayushman, et al.
Published: (2025)
by: Raghuvanshi, Ayushman, et al.
Published: (2025)
Re-evaluating the Advancements of Heterophilic Graph Learning
by: Luan, Sitao, et al.
Published: (2024)
by: Luan, Sitao, et al.
Published: (2024)
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
by: Wang, Kun, et al.
Published: (2024)
by: Wang, Kun, et al.
Published: (2024)
Assessing the Limits of In-Context Learning beyond Functions using Partially Ordered Relation
by: Dutta, Debanjan, et al.
Published: (2025)
by: Dutta, Debanjan, et al.
Published: (2025)
Hyperbolic Fuzzy C-Means with Adaptive Weight-based Filtering for Efficient Clustering
by: Das, Swagato, et al.
Published: (2025)
by: Das, Swagato, et al.
Published: (2025)
Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
by: Fuchsgruber, Dominik, et al.
Published: (2025)
by: Fuchsgruber, Dominik, et al.
Published: (2025)
Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic and Heterophilic Graphs
by: Yang, Yachao, et al.
Published: (2024)
by: Yang, Yachao, et al.
Published: (2024)
HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations
by: Zhang, Shuaicheng, et al.
Published: (2025)
by: Zhang, Shuaicheng, et al.
Published: (2025)
Rebalancing with Calibrated Sub-classes (RCS): A Statistical Fusion-based Framework for Robust Imbalanced Classification across Modalities
by: Mondal, Priyobrata, et al.
Published: (2025)
by: Mondal, Priyobrata, et al.
Published: (2025)
APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness
by: Mondal, Priyobrata, et al.
Published: (2025)
by: Mondal, Priyobrata, et al.
Published: (2025)
On Robust Cross Domain Alignment
by: Chakrabarty, Anish, et al.
Published: (2024)
by: Chakrabarty, Anish, et al.
Published: (2024)
ATLAS: Adaptive Topology-based Learning at Scale for Homophilic and Heterophilic Graphs
by: Kundu, Turja, et al.
Published: (2025)
by: Kundu, Turja, et al.
Published: (2025)
Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
by: Wang, Xiangmeng, et al.
Published: (2026)
by: Wang, Xiangmeng, et al.
Published: (2026)
GCL-OT: Graph Contrastive Learning with Optimal Transport for Heterophilic Text-Attributed Graphs
by: Ren, Yating, et al.
Published: (2025)
by: Ren, Yating, et al.
Published: (2025)
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?
by: Xu, Yanchen, et al.
Published: (2024)
by: Xu, Yanchen, et al.
Published: (2024)
Representation Learning on Heterophilic Graph with Directional Neighborhood Attention
by: Lu, Qincheng, et al.
Published: (2024)
by: Lu, Qincheng, et al.
Published: (2024)
Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
by: Ni, Hang, et al.
Published: (2025)
by: Ni, Hang, et al.
Published: (2025)
Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs
by: Gupta, Arman, et al.
Published: (2025)
by: Gupta, Arman, et al.
Published: (2025)
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning
by: Lu, Qincheng, et al.
Published: (2024)
by: Lu, Qincheng, et al.
Published: (2024)
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network
by: Zheng, Yilun, et al.
Published: (2024)
by: Zheng, Yilun, et al.
Published: (2024)
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
by: Liang, Langzhang, et al.
Published: (2024)
by: Liang, Langzhang, et al.
Published: (2024)
HeNCler: Node Clustering in Heterophilous Graphs via Learned Asymmetric Similarity
by: Achten, Sonny, et al.
Published: (2024)
by: Achten, Sonny, et al.
Published: (2024)
Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs
by: Qiu, Ruizhong, et al.
Published: (2025)
by: Qiu, Ruizhong, et al.
Published: (2025)
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
by: Yang, Jinluan, et al.
Published: (2024)
by: Yang, Jinluan, et al.
Published: (2024)
Similar Items
-
Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks
by: Bose, Kushal, et al.
Published: (2025) -
Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration
by: Pratihar, Arghya, et al.
Published: (2025) -
Transformers Are Universally Consistent
by: Ghosh, Sagar, et al.
Published: (2025) -
On the Universal Statistical Consistency of Expansive Hyperbolic Deep Convolutional Neural Networks
by: Ghosh, Sagar, et al.
Published: (2024) -
A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights
by: Pan, Shubhayan, et al.
Published: (2025)