Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for
the important task of node clustering has not been fully exploited. In particular, most state-of …

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 graph representation learning is an effective approach to analyze the complex
patterns in real-world signed graphs with the co-existence of positive and negative links …

Signed networks are graphs where edges are annotated with a positive or negative sign,
indicating whether an edge interaction is friendly or antagonistic. Signed networks can be …

Signed graphs encode positive (attractive) and negative (repulsive) relations between
nodes. We extend spectral clustering to signed graphs via the one-parameter family of …

The time proximity of trades across stocks reveals interesting topological structures of the
equity market in the United States. In this article, we investigate how such concurrent cross …

Real-world networks often come with side information that can help to improve the
performance of network analysis tasks such as clustering. Despite a large number of …

Signed network embedding (SNE) has received considerable attention in recent years. A
mainstream idea of SNE is to learn node representations by estimating the ratio of sampling …

We study the problem of k-way clustering in signed graphs. Considerable attention in recent
years has been devoted to analyzing and modeling signed graphs, where the affinity …

Political conflict is an essential element of democratic systems, but can also threaten their
existence if it becomes too intense. This happens particularly when most political issues …