Network as a computer: ranking paths to find flows

We explore a simple mathematical model of network computation, based on Markov chains. Similar models apply to a broad range of computational phenomena, arising in networks of computers, as well as in genetic, and neural nets, in social networks, and so on. The main problem of interaction with such spontaneously evolving computational systems is that the data are not uniformly structured. An interesting approach is to try to extract the semantical content of the data from their distribution among the nodes. A concept is then identified by finding the community of nodes that share it. The task of data structuring is thus reduced to the task of finding the network communities, as groups of nodes that together perform some non-local data processing. Towards this goal, we extend the ranking methods from nodes to paths. This allows us to extract some information about the likely flow biases from the available static information about the network.Comment: 12 pages, CSR 200

Efficient computing of radius-bounded κ-cores

© 2018 IEEE. Driven by real-life applications in geo-social networks, in this paper, we investigate the problem of computing the radius-bounded k-cores (RB-k-cores) that aims to find cohesive subgraphs satisfying both social and spatial constraints on large geo-social networks. In particular, we use k-core to ensure the social cohesiveness and we use a radius-bounded circle to restrict the locations of users in a RB-k-core. We explore several algorithmic paradigms to compute RB-k-cores, including a triple vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating circle-based paradigm is further enhanced with several pruning techniques to achieve better efficiency. The experimental studies conducted on both real and synthetic datasets demonstrate that our proposed rotating-circle-based algorithms can compute all RB-k-cores very efficiently. Moreover, it can also be used to compute the minimum-circle-bounded k-core and significantly outperforms the existing techniques for computing the minimum circle-bounded k-core

Experts and Decision Making: First Steps Towards a Unifying Theory of Decision Making in Novices, Intermediates and Experts

Expertise research shows quite ambiguous results on the abilities of experts in judgment and decision making (JDM) classic models cannot account for. This problem becomes even more accentuated if different levels of expertise are considered. We argue that parallel constraint satisfaction models (PCS) might be a useful base to understand the processes underlying expert JDM and the hitherto existing, differentiated results from expertise research. It is outlined how expertise might influence model parameters and mental representations according to PCS. It is discussed how this differential impact of expertise on model parameters relates to empirical results showing quite different courses in the development of expertise; allowing, for example, to predict under which conditions intermediates might outperform experts. Methodological requirements for testing the proposed unifying theory under complex real-world conditions are discussed.Judgment and Decision Making, Expertise, Intermediate Effects, Parallel Constraint Satisfaction, Mental Representation