Results 21 to 30 of about 719 (136)

Effective Eigendecomposition based Graph Adaptation for Heterophilic Networks

CoRR, 2021
arXiv admin note: text overlap with arXiv:2106 ...
Vijay Lingam, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam   +3 more
openaire   +2 more sources

GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs

CoRR, 2022
In graph learning, there have been two predominant inductive biases regarding graph-inspired architectures: On the one hand, higher-order interactions and message passing work well on homophilous graphs and are leveraged by GCNs and GATs. Such architectures, however, cannot easily scale to large real-world graphs.
Marios Papachristou, Rishab Goel, Frank Portman, Matthew Miller, Rong Jin   +4 more
openaire   +2 more sources

The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly. While this underlying assumption is frequently adopted, it is not universally applicable, which can result in potential shortcomings in ...
Kun Wang 0056, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li 0001, Shirui Pan, Wei Huang 0034, Yuxuan Liang 0002   +8 more
openaire   +2 more sources

GCNH: A Simple Method For Representation Learning On Heterophilous Graphs

2023 International Joint Conference on Neural Networks (IJCNN), 2023
Accepted at 2023 International Joint Conference on Neural Networks (IJCNN)
Andrea Cavallo, Claas Grohnfeldt, Michele Russo, Giulio Lovisotto, Luca Vassio   +4 more
openaire   +3 more sources

AFMF: adaptive fusion of multi-hop neighborhood features in graph convolutional network

Journal of King Saud University: Computer and Information Sciences
Graph-structured data has been widely used in modern information management systems. Effectively extracting the latent structural and semantic relationships between nodes in the graph is a key research challenge.
Kang Liu, Xinyu Li, Lian Liu, Zhihao Xv, Aohang Pei, Runshi Ji   +5 more
doaj   +1 more source

Nutritionists and Dietitians as Key Actors in Local Food Systems: A Systems‐Based Social Network Analysis

Journal of Human Nutrition and Dietetics, Volume 39, Issue 2, April 2026.
ABSTRACT Background Nutritionists and dietitians play an important role in supporting sustainable food systems and diets; however, their involvement in local alternative food networks remains poorly defined. These actors are well placed to apply their extensive expertise in food, nutrition and health to the transformation of the contemporary food ...
Suzy Pickles, Anita Stefoska‐Needham, Katherine Cullerton, Katherine Kent, Karen Charlton   +4 more
wiley   +1 more source

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

IEEE Access
Knowledge Graphs are widely used to represent knowledge structures in complex domains. In most real-world scenarios, these knowledge structures are dynamic.
Siraj Munir, Rauf Ahmed Shams Malick, Stefano Ferretti   +2 more
doaj   +1 more source

Imprecision in Vision: Lessons From Neural Circuits in the Fly

BioEssays, Volume 48, Issue 3, March 2026.
Visual systems are considered regular, retinotopic structures. Here, we summarize findings, including evidence from recent connectomes, that demonstrate cellular and synaptic heterogeneity in the Drosophila visual system —from regional specialization and stochastic photoreceptor patterns to heterogeneous synaptic connectivity.
Mathias F. Wernet, Marion Silies
wiley   +1 more source

Permutation Equivariant Graph Framelets for Heterophilous Graph Learning

IEEE Transactions on Neural Networks and Learning Systems
The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network models and suggests aggregations beyond the 1-hop neighborhood. In this paper, we develop a new way to implement multi-scale extraction via constructing Haar-type graph framelets with desired properties ...
Jianfei Li, Ruigang Zheng, Han Feng, Ming Li 0065, Xiaosheng Zhuang   +4 more
openaire   +3 more sources

Calorie Restriction Effects on Aging, Learning Performance, and Transcription in Aged Aplysia californica

Genes, Brain and Behavior, Volume 25, Issue 1, February 2026.
Buccal ganglia inter‐ and motoneuronal transcriptional changes were investigated after learning food is inedible (LFI) in Aplysia californica reared on different diets at two ages. Those reared on calorie restriction showed delayed signs of aging and maintained their performance in LFI when aged while those reared on ad‐lib did not. ABSTRACT Along with
Eric C. Randolph, Lynne A. Fieber
wiley   +1 more source