3 research outputs found
Discovering Communities of Community Discovery
Discovering communities in complex networks means grouping nodes similar to
each other, to uncover latent information about them. There are hundreds of
different algorithms to solve the community detection task, each with its own
understanding and definition of what a "community" is. Dozens of review works
attempt to order such a diverse landscape -- classifying community discovery
algorithms by the process they employ to detect communities, by their
explicitly stated definition of community, or by their performance on a
standardized task. In this paper, we classify community discovery algorithms
according to a fourth criterion: the similarity of their results. We create an
Algorithm Similarity Network (ASN), whose nodes are the community detection
approaches, connected if they return similar groupings. We then perform
community detection on this network, grouping algorithms that consistently
return the same partitions or overlapping coverage over a span of more than one
thousand synthetic and real world networks. This paper is an attempt to create
a similarity-based classification of community detection algorithms based on
empirical data. It improves over the state of the art by comparing more than
seventy approaches, discovering that the ASN contains well-separated groups,
making it a sensible tool for practitioners, aiding their choice of algorithms
fitting their analytic needs
Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey
This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose four complexity dimensions for the network representation and five generations of models for the dynamics modelling to describe the increasing complexity level of the CNS that will be developed towards achieving DT (e.g. CNS dynamics modelled offline in the 1st generation v.s. CNS dynamics modelled simultaneously with a two-way real time feedback between reality and the CNS in the 5th generation). Based on that, we propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives and create unified assessment criteria to evaluate their respective capabilities of reaching such an ultimate goal. Using the proposed criteria, we also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs. Finally, we identify and propose potential directions and ways of building a DT-orientated CNS based on the convergence and integration of CNS and DT utilising a variety of cross-disciplinary techniques