Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

Abstract The area of Data Analytics on graphs promises a paradigm shift, as we approach
information processing of new classes of data which are typically acquired on irregular but …

Hypergraph visualization has many applications in network data analysis. Recently, a
polygon-based representation for hypergraphs has been proposed with demonstrated …

Spectral graph sparsification aims to find ultra-sparse subgraphs whose Laplacian matrix
can well approximate the original Laplacian eigenvalues and eigenvectors. In recent years …

We present a novel graph sparsification approach for semisupervised learning on
undirected attributed graphs. The main challenge is to retain few edges while minimize the …

A network backbone provides a useful sparse representation of a weighted network by
keeping only its most important links, permitting a range of computational speedups and …

This work introduces a highly scalable spectral graph densification (SGL) framework for
learning resistor networks with linear measurements, such as node voltages and currents …

图可视化是图数据的直观表示, 随着图数据的广泛应用, 合适的图可视化能够使用户对图数据的
理解更加深入和高效. 但随着图数据量级的增长, 图可视化布局面临着计算时间长 …

Network sparsification represents an essential tool to extract the core of interactions
sustaining both networks dynamics and their connectedness. In the case of infectious …

Graph Neural Networks (GNNs) learn from graph-structured data by passing local messages
between neighboring nodes along edges on certain topological layouts. Typically, these …