scholarly journals Expected Emergent Algorithmic Creativity and Integration in Dynamic Complex Networks

2018 ◽  
Author(s):  
Felipe S. Abrahão ◽  
Klaus Wehmuth ◽  
Artur Ziviani
Keyword(s):  
Phase Transition ◽  
Complex Networks ◽  
Theoretical Investigation ◽  
Network Topology ◽  
Communication Protocol ◽  
Network Size ◽  
Turing Machines ◽  
Emergence Of Complexity ◽  
Unlimited Amount ◽  
Over Time

We present a theoretical investigation of the emergence of complexity or irreducible information in networked computable systems when the network topology may change over time. For this purpose, we build a network model in which nodes are randomly generated Turing machines that obey a communication protocol of imitation of the fittest neighbor. Then, we show that there are topological conditions that trigger a phase transition in which eventually these networked computable systems begin to produce an unlimited amount of bits of expected emergent algorithmic complexity, creativity and integration as the network size goes to infinity.

scholarly journals Transtemporal edges and crosslayer edges in incompressible high-order networks

2019 ◽  
Author(s):  
Felipe Abrahão ◽  
Klaus Wehmuth ◽  
Artur Ziviani
Keyword(s):  
Standard Deviation ◽  
Complex Networks ◽  
Theoretical Investigation ◽  
Real World ◽  
Dynamic Networks ◽  
Network Size ◽  
High Order ◽  
Topological Properties ◽  
High K ◽  
Short Diameter

This work presents some outcomes of a theoretical investigation of incompressible high-order networks defined by a generalized graph represen tation. We study some of their network topological properties and how these may be related to real world complex networks. We show that these networks have very short diameter, high k-connectivity, degrees of the order of half of the network size within a strong-asymptotically dominated standard deviation, and rigidity with respect to automorphisms. In addition, we demonstrate that incompressible dynamic (or dynamic multilayered) networks have transtemporal (or crosslayer) edges and, thus, a snapshot-like representation of dynamic networks is inaccurate for capturing the presence of such edges that compose underlying structures of some real-world networks.

scholarly journals A novel similarity measure for missing link prediction in social networks

2020 ◽  
Vol 19 (2) ◽  
pp. 1071
Author(s):  
Gogulamudi Naga Chandrika ◽  
E. Srinivasa Reddy
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Similarity Measure ◽  
Network Theory ◽  
Link Prediction ◽  
Prediction Methods ◽  
Missing Link ◽  
The Social ◽  
Hidden Patterns ◽  
Over Time

<p><span>Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. </span><span>We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.</span></p>

scholarly journals Discontinuous phase transition in a core contact process on complex networks

2015 ◽  
Vol 17 (2) ◽  
pp. 023039 ◽  
Author(s):  
Huiseung Chae ◽  
Soon-Hyung Yook ◽  
Yup Kim
Keyword(s):  
Phase Transition ◽  
Complex Networks ◽  
Contact Process ◽  
Discontinuous Phase

scholarly journals PROBING COMPLEX NETWORKS FROM MEASURED TIME SERIES

2012 ◽  
Vol 22 (10) ◽  
pp. 1250236 ◽  
Author(s):  
LIANG HUANG ◽  
YING-CHENG LAI ◽  
MARY ANN F. HARRISON
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Network Topology ◽  
Efficient Method ◽  
Relative Importance ◽  
Computationally Efficient ◽  
Heuristic Argument ◽  
Principal Eigenvector ◽  
Measured Time ◽  
Potential Applications

We propose a method to detect nodes of relative importance, e.g. hubs, in an unknown network based on a set of measured time series. The idea is to construct a matrix characterizing the synchronization probabilities between various pairs of time series and examine the components of the principal eigenvector. We provide a heuristic argument indicating the existence of an approximate one-to-one correspondence between the components and the degrees of the nodes from which measurements are obtained. The striking finding is that such a correspondence appears to be quite robust, which holds regardless of the detailed node dynamics and of the network topology. Our computationally efficient method thus provides a general means to address the important problem of network detection, with potential applications in a number of fields.

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