6 research outputs found
Precise influence evaluation in complex networks
Evaluating node influence is fundamental for identifying key nodes in complex
networks. Existing methods typically rely on generic indicators to rank node
influence across diverse networks, thereby ignoring the individualized features
of each network itself. Actually, node influence stems not only from general
features but the multi-scale individualized information encompassing specific
network structure and task. Here we design an active learning architecture to
predict node influence quantitively and precisely, which samples representative
nodes based on graph entropy correlation matrix integrating multi-scale
individualized information. This brings two intuitive advantages: (1)
discovering potential high-influence but weak-connected nodes that are usually
ignored in existing methods, (2) improving the influence maximization strategy
by deducing influence interference. Significantly, our architecture
demonstrates exceptional transfer learning capabilities across multiple types
of networks, which can identify those key nodes with large disputation across
different existing methods. Additionally, our approach, combined with a simple
greedy algorithm, exhibits dominant performance in solving the influence
maximization problem. This architecture holds great potential for applications
in graph mining and prediction tasks
Reconstructing networks
Complex networks datasets often come with the problem of missing information:
interactions data that have not been measured or discovered, may be affected by
errors, or are simply hidden because of privacy issues. This Element provides
an overview of the ideas, methods and techniques to deal with this problem and
that together define the field of network reconstruction. Given the extent of
the subject, we shall focus on the inference methods rooted in statistical
physics and information theory. The discussion will be organized according to
the different scales of the reconstruction task, that is, whether the goal is
to reconstruct the macroscopic structure of the network, to infer its mesoscale
properties, or to predict the individual microscopic connections.Comment: 107 pages, 25 figure
Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations
The network structure (or topology) of a dynamical network is often
unavailable or uncertain. Hence, we consider the problem of network
reconstruction. Network reconstruction aims at inferring the topology of a
dynamical network using measurements obtained from the network. In this
technical note we define the notion of solvability of the network
reconstruction problem. Subsequently, we provide necessary and sufficient
conditions under which the network reconstruction problem is solvable. Finally,
using constrained Lyapunov equations, we establish novel network reconstruction
algorithms, applicable to general dynamical networks. We also provide
specialized algorithms for specific network dynamics, such as the well-known
consensus and adjacency dynamics.Comment: 8 page