77 research outputs found
On analysis of complex network dynamics – changes in local topology
Social networks created based on data gathered in various computer systems are structures that constantly evolve. The nodes and their connections change because they are influenced by the external to the network events.. In this work we present a new approach to the description and quantification of patterns of complex dynamic social networks illustrated with the data from the Wroclaw University of Technology email dataset. We propose an approach based on discovery of local network connection patterns (in this case triads of nodes) as well as we measure and analyse their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads, after that we show how it can help to discover the dynamic patterns of network evolution. One of the main issues when investigating the dynamical process is the selection of the time window size. Thus, the goal of this paper is also to investigate how the size of time window influences the shape of TTM and how the dynamics of triad number change depending on the window size. We have shown that, however the link stability in the network is low, the dynamic network evolution pattern expressed by the TTMs is relatively stable, and thus forming a background for fine-grained classification of complex networks dynamics. Our results open also vast possibilities of link and structure prediction of dynamic networks. The future research and applications stemming from our approach are also proposed and discussed
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
Link Prediction Based on Subgraph Evolution in Dynamic Social Networks
We propose a new method for characterizing the dynamics of complex networks with its application to the link prediction problem. Our approach is based on the discovery of network subgraphs (in this study: triads of nodes) and measuring their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads found in the network, then we show how it can help to discover and quantify the dynamic patterns of network evolution. We also propose the application of TTM to link prediction with an algorithm (called TTM-predictor) which shows good performance, especially for sparse networks analyzed in short time scales. The future applications and research directions of our approach are also proposed and discussed
Exploring the requirements process for a complex, adaptive system in a high risk software development environment
This work ties together research from a number of different areas to show how the develop-ment of a complex adaptive system for an industrial company has a number of difficulties given the current state of the art. The INFER system which is a Complex Adaptive System (CAS) has a number of attributes which mean that current requirements and indeed develop-ment processes are not able to cope with them adequately. A CAS can be recognised by the fact that it consists of a number of agents acting together dynamically resulting in emergent behaviour. This emergent behaviour cannot be predicted and thus, along with other phenom-ena such as reaction to and with the environment and deciding the different responsibilities of the components means that the requirements process for such a system is a current research area. A retrospective case study is underway to capture the rich data available from the ex-periences of building such a syste
Hybrid Link Prediction Model
In network science several topology--based link prediction methods have been developed so far. The classic social network link prediction approach takes as an input a snapshot of a whole network. However, with human activities behind it, this social network keeps changing. In this paper, we consider link prediction problem as a time--series problem and propose a hybrid link prediction model that combines eight structure-based prediction methods and self-adapts the weights assigned to each included method. To test the model, we perform experiments on two real world networks with both sliding and growing window scenarios. The results show that our model outperforms other structure--based methods when both precision and recall of the prediction results are considered
Probabilistic Approach to Structural Change Prediction in Evolving Social Networks
We propose a predictive model of structural
changes in elementary subgraphs of social network based on
Mixture of Markov Chains. The model is trained and verified
on a dataset from a large corporate social network analyzed
in short, one day-long time windows, and reveals distinctive
patterns of evolution of connections on the level of local
network topology. We argue that the network investigated in
such short timescales is highly dynamic and therefore immune
to classic methods of link prediction and structural analysis,
and show that in the case of complex networks, the dynamic
subgraph mining may lead to better prediction accuracy. The
experiments were carried out on the logs from the Wroclaw
University of Technology mail server
Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems
Building models of Complex Networked Systems (CNS) that can accurately
represent reality forms an important research area. To be able to reflect real
world systems, the modelling needs to consider not only the intensity of
interactions between the entities but also features of all the elements of the
system. This study aims to improve the expressive power of node features in
Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with heterogeneous
feature representation principles. This involves representing features with
crisp feature values and fuzzy sets, each describing the objective and the
subjective inductions of the nodes' features and feature differences. Our
empirical analysis builds DT-CNSs to recreate realistic physical contact
networks in different countries from real node feature distributions based on
various representation principles and an optimised feature preference. We also
investigate their respective disaster resilience to an epidemic outbreak
starting from the most popular node. The results suggest that the increasing
flexibility of feature representation with fuzzy sets improves the expressive
power and enables more accurate modelling. In addition, the heterogeneous
features influence the network structure and the speed of the epidemic
outbreak, requiring various mitigation policies targeted at different people
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