arXiv AI July 3, 2026 By Di Wu, Huan Liu, Zhixiang Chi, Yuanhao Yu, Konstantinos N. Plataniotis, Yang Wang

Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space

arXiv:2607. 02166v1 Announce Type: cross Abstract: The rapid advancements in using neural networks as implicit data representations have attracted significant interest in developing machine learning methods that analyze and process the weight spaces of other neural networks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 7

Dynamic Heterogeneous Graph Representation Learning: A Survey

The article surveys Dynamic Heterogeneous Graph Representation Learning (DHGRL), a field that tackles the challenges of modeling evolving, multi‑type networks. It introduces a unified definition covering both discrete‑time and continuous‑time DHGs, and proposes an algorithm‑centric taxonomy that groups methods into embedding‑based, GNN‑based, and Transformer‑based approaches, highlighting their biases toward temporal granularity. The survey also reviews key applications, datasets, benchmarks, and outlines future research directions.

By Huan Liu, Pengfei Jiao, Jie Yin, Hongjiang Chen, Zhidong Zhao

llms rag benchmarks

arXiv Machine Learning

Jul 7

WeightCLIP: Aligning Datasets and Models for Weight Space Learning

arXiv:2607. 03551v1 Announce Type: new Abstract: Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks.

By Aron Asefaw, Konstantinos Tzevelekakis, Damian Falk, L\'eo Meynent, Damian Borth

diffusion fine-tuning

arXiv Machine Learning

Jul 22

Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs

arXiv:2607. 18412v1 Announce Type: new Abstract: Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems.

By Huizhe Zhang, Yuchang Zhu, Huazhen Zhong, Liang Chen, Zibin Zheng

rag

arXiv Machine Learning

Jun 3

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer

arXiv:2506. 00431v2 Announce Type: replace Abstract: Due to the proficiency of self-attention mechanisms (SAMs) in capturing dependencies in sequence modeling, several existing dynamic graph neural networks (DGNNs) utilize Transformer architectures with various encoding designs to capture sequential evolutions of dynamic graphs.

By Jie Peng, Zhewei Wei, Yuhang Ye

llms rag benchmarks safety

arXiv Machine Learning

Jul 23

Geometry-Guided Generative Representation for Functional Brain Graphs

arXiv:2511. 04539v2 Announce Type: replace-cross Abstract: In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties covary and partially overlap across individuals and conditions.

By Subati Abulikemu, Tiago Azevedo, Michail Mamalakis, John Suckling

llms rag diffusion safety

Hugging Face Trending Papers

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Repurposing Unified Topological Signatures for Graph Representation Learning

Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn graph representations. However, their discriminative...

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Dynamic Heterogeneous Graph Representation Learning: A Survey

WeightCLIP: Aligning Datasets and Models for Weight Space Learning

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TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer

Geometry-Guided Generative Representation for Functional Brain Graphs

Repurposing Unified Topological Signatures for Graph Representation Learning