Learning on graphs is a subject of great interest due to the abundance of relational data
from real-world systems. Many of these systems involve higher-order interactions (super …

Despite the fact that many important problems (including clustering) can be described using
hypergraphs, theoretical foundations as well as practical algorithms using hypergraphs are …

Many real-world systems consist of entities that exhibit complex group interactions rather
than simple pairwise relationships; such multi-way relations are more suitably modeled …

In AI and Web communities, modularity-based graph clustering algorithms are being applied
to various applications. However, existing algorithms are not applied to large graphs …

We present a novel hierarchical graph clustering algorithm inspired by modularity-based
clustering techniques. The algorithm is agglomerative and based on a simple distance …

Many social networks and complex systems are found to be naturally divided into clusters of
densely connected nodes, known as community structure (CS). Finding CS is one of …

Modularity clustering is a recently introduced clustering objective function for graph
clustering. It has been widely used in bioinformatics and social networks. Its relation to data …

Hypergraph partitioning is an important problem in machine learning, computer vision and
network analytics. A widely used method for hypergraph partitioning relies on minimizing a …

Graphs are an important tool for modeling data in many diverse domains. Recent increase in
sensor technology and deployment, the adoption of online services, and the scale of VLSI …

Modularity is a recently introduced quality measure for graph clusterings. It has immediately
received considerable attention in several disciplines, particularly in the complex systems …