Nectins are members of the Ig superfamily that mediate cell-cell adhesion through homophilic and heterophilic interactions. We have determined the crystal structure of the nectin-2 homodimer at 1.3 Å resolution. Structural analysis and complementary mutagenesis studies reveal the basis for recognition and selectivity among the nectin family members. Notably, the close proximity of charged residues at the dimer interface is a major determinant of the binding affinities associated with homophilic and heterophilic interactions within the nectin family. Our structural and biochemical data provide a mechanistic basis to explain stronger heterophilic versus weaker homophilic interactions among these family members and also offer insights into nectin-mediated transinteractions between engaging cells.
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http://dx.doi.org/10.1073/pnas.1212912109 | DOI Listing |
Neural Netw
December 2024
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address:
Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs. However, they cannot generalize well to real-world graphs with different levels of homophily. In response, the Poisson-Charlier Network (PCNet) (Li et al.
View Article and Find Full Text PDFEur J Clin Invest
December 2024
Division of Gastroenterology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Neural Netw
January 2025
Department of Computer Science and Artificial Intelligence, Alicante, Spain.
In the ideal (homophilic) regime of vanilla GNNs, nodes belonging to the same community have the same label: most of the nodes are harmonic (their unknown labels result from averaging those of their neighbors given some labeled nodes). In other words, heterophily (when neighboring nodes have different labels) can be seen as a "loss of harmonicity". In this paper, we define "structural heterophily" in terms of the ratio between the harmonicity of the network (Laplacian Dirichlet energy) and the harmonicity of its homophilic version (the so-called "ground" energy).
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
December 2024
While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However, real-world networks typically exhibit both homophilic and heterophilic linking patterns, wherein adjacent nodes may share dissimilar attributes and distinct labels. Therefore, GNNs smoothing node proximity holistically may aggregate both task-relevant and irrelevant (even harmful) information, limiting their ability to generalize to heterophilic graphs and potentially causing non-robustness.
View Article and Find Full Text PDFKnowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be accomplished efficiently and without losing too much prediction accuracy. Recently, many attempts have been made by applying the KD mechanism to graph representation learning models such as graph neural networks (GNNs) to accelerate the model's inference speed via student models. However, many existing KD-based GNNs utilize multilayer perceptron (MLP) as a universal approximator in the student model to imitate the teacher model's process without considering the graph knowledge from the teacher model.
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