Abstract Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the
intersection of graph clustering and semi-supervised learning (SSL), offering innovative …

Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …

Signed and directed networks are ubiquitous in real-world applications. However, there has
been relatively little work proposing spectral graph neural networks (GNNs) for such …

Spectral methods for graph neural networks (GNNs) have achieved great success. Despite
their success, many works have shown that existing approaches are mainly focused on low …

Networks are ubiquitous in many real-world applications (eg, social networks encoding
trust/distrust relationships, correlation networks arising from time series data). While many …

Signed graphs model complex relations using both positive and negative edges. Signed
graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the …

This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN)
capable of handling both undirected and directed graphs with weights not restricted in sign …

Polarized communities search aims at locating query-dependent communities, in which
mostly nodes within each community form intensive positive connections, while mostly …

Signed graphs can represent complex systems of positive and negative relationships such
as trust or preference in various domains. Learning node representations is indispensable …

Community detection is an essential topic in network analysis, which aims to divide a
network into multiple subgraphs to mine potential information. However, most existing …