36 research outputs found
Laplacian paths in complex networks: Information core emerges from entropic transitions
Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information
pathways are expected to pervade these scales reflecting structural insights that are not manifest from analyses
of the network topology. Moreover, small-world effects correlate with the different network hierarchies complicating
the identification of coexisting mesoscopic structures and functional cores.We present a communicability
analysis of effective information pathways throughout complex networks based on information diffusion to shed
further light on these issues. We employ a variety of brand-new theoretical techniques allowing for: (i) bring
the theoretical framework to quantify the probability of information diffusion among nodes, (ii) identify critical
scales and structures of complex networks regardless of their intrinsic properties, and (iii) demonstrate their
dynamical relevance in synchronization phenomena. By combining these ideas, we evidence how the information
flow on complex networks unravels different resolution scales. Using computational techniques, we focus on
entropic transitions, uncovering a generic mesoscale object, the information core, and controlling information
processing in complex networks. Altogether, this study sheds much light on allowing new theoretical techniques
paving the way to introduce future renormalization group approaches based on diffusion distances
A Network Science perspective of Graph Convolutional Networks: A survey
The mining and exploitation of graph structural information have been the
focal points in the study of complex networks. Traditional structural measures
in Network Science focus on the analysis and modelling of complex networks from
the perspective of network structure, such as the centrality measures, the
clustering coefficient, and motifs and graphlets, and they have become basic
tools for studying and understanding graphs. In comparison, graph neural
networks, especially graph convolutional networks (GCNs), are particularly
effective at integrating node features into graph structures via neighbourhood
aggregation and message passing, and have been shown to significantly improve
the performances in a variety of learning tasks. These two classes of methods
are, however, typically treated separately with limited references to each
other. In this work, aiming to establish relationships between them, we provide
a network science perspective of GCNs. Our novel taxonomy classifies GCNs from
three structural information angles, i.e., the layer-wise message aggregation
scope, the message content, and the overall learning scope. Moreover, as a
prerequisite for reviewing GCNs via a network science perspective, we also
summarise traditional structural measures and propose a new taxonomy for them.
Finally and most importantly, we draw connections between traditional
structural approaches and graph convolutional networks, and discuss potential
directions for future research