Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …

Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

We give upper and lower bounds on the information-theoretic threshold for community
detection in the stochastic block model. Specifically, consider a symmetric stochastic block …

The virtual brand community has become an important marketing tool for companies. A
successful brand community marketing strategy should attract a large number of consumers …

Methods for ranking the importance of nodes in a network have a rich history in machine
learning and across domains that analyze structured data. Recent work has evaluated these …

We consider the problem of community detection or clustering in the labeled Stochastic
Block Model (LSBM) with a finite number $ K $ of clusters of sizes linearly growing with the …

A plethora of exhaustive studies have proved that the community detection merely based on
topological information often leads to relatively low accuracy. Several approaches aim to …

Community detection methods attempt to divide a network into groups of nodes that share
similar properties, thus revealing its large-scale structure. A major challenge when …

In social networks, consumers gather to form brand communities, and the community
structure significantly impacts the dissemination of brand information. Which communication …