Link prediction is a technique to forecast future new or missing relationships between nodes
based on the current network information. However, the link prediction in monoplex …

Most methods proposed to uncover communities in complex networks rely on combinatorial
graph properties. Usually an edge-counting quality function, such as modularity, is optimized …

Graph neural networks (GNNs) extends the functionality of traditional neural networks to
graph-structured data. Similar to CNNs, an optimized design of graph convolution and …

Complex social network is a kind of relationship system composed of many nodes according
to social relations. Community detection helps scholars to understand this network topology …

This paper provides a comprehensive analysis of the following optimization problem:
Maximize the entropy rate generated by a Markov chain over a connected graph of order n …

Many of the structural characteristics of a network depend on the connectivity with and within
the hubs. These dependencies can be related to the degree of a node and the number of …

We perform an in-depth study for mean first-passage time (MFPT)—a primary quantity for
random walks with numerous applications—of maximal-entropy random walks (MERW) …

Maximum entropy random walks (MERWs) are maximally dispersing and play a key role in
optimizing information spreading in various contexts. However, building MERWs comes at …

Random walks have been intensively studied on regular and complex networks, which are
used to represent pairwise interactions. Nonetheless, recent works have demonstrated that …

Our investigation centres on assessing the importance of a biased parameter (α) in a
multiplex Markov chain (MMC) model that is characterized by biased random walks in …