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Found 7 papers, 4 papers with code
Interpretable Subgraph Feature Extraction for Hyperlink Prediction
In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features.
Neural activity in quarks language: Lattice Field Theory for a network of real neurons
Brain-computer interfaces surged extraordinary developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework.
A Graph Dynamics Prior for Relational Inference
In this work, we propose a \textit{graph dynamics prior} (GDP) for relational inference.
Homophily modulates double descent generalization in graph convolution networks
Motivated by experimental observations of ``transductive'' double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model.
Neural Link Prediction with Walk Pooling
Instead of extracting transition probabilities from the original graph, it computes the transition matrix of a "predictive" latent graph by applying attention to learned features; this may be interpreted as feature-sensitive topology fingerprinting.
Ranked #1 on
Link Prediction
on Pubmed
Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks
Our work provides insights on the importance of nodes in the multiplex network and gives a feasible framework to investigate influential spreaders in the asymmetrically coevolving dynamics.
Physics and Society
Link Prediction via Matrix Completion
Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years.
