Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …

Immutability, decentralization, and linear promoted scalability make the sharded blockchain
a promising solution, which can effectively address the trust issue in the large-scale Internet …

First-principle network models are crucial to understanding the intricate topology of real
complex networks. Although modelling efforts have been quite successful in undirected …

Ecosystems represent archetypal complex dynamical systems, often modelled by coupled
differential equations of the form dxidt= xi ϕ i (x 1,…, x N), where N represents the number of …

We address the problem of using observational data to estimate peer contagion effects, the
influence of treatments applied to individuals in a network on the outcomes of their …

Correlation clustering is a fundamental optimization problem at the intersection of machine
learning and theoretical computer science. Motivated by applications to big data processing …

Can we use machine learning to compress graph data? The absence of ordering in graphs
poses a significant challenge to conventional compression algorithms, limiting their …