Community detection in multiplex networks using locally adaptive random walks

Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex. The algorithm is based on a random walk on the multiplex, and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers. Based on this random walk, a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion. We present experimental results using networks simulated under various scenarios to showcase the performance of LART in comparison to related community detection algorithms

A network characterization of the interbank exposures in Peru

After the Global Financial Crisis (GFC), systemic risk measurement became crucial for policy makers as well as for academics. We have witnessed an important increase in the number of methodologies proposed. Among such proposals, DebtRank arose as perhaps one of the most relevant in this context, as it resorts to network modeling and captures the all-important aspect of interconnectedness in the financial system. Additionally, within the network modeling approach, there is the multilayer approach, which provides additional insights on the decomposition of systemic risk. In this paper, we apply a multilayer network analysis to study systemic risk in the Peruvian banking system by utilizing DebtRank centrality. The main contributions of this work are as follows: i) It fully characterizes the multilayer exposure network of the Peruvian banking system, and ii) it obtains the systemic risk profile of the banking system according to different types of exposures

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace