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

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

Quantifying Social Network Dynamics

The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics. The following paper focuses on the definition of differences between network snapshots by means of Graph Differential Tuple. These differences enable to calculate the diverse distance measures as well as to investigate the speed of changes. Four separate measures are suggested in the paper with experimental study on real social network data.Comment: In proceedings of the 4th International Conference on Computational Aspects of Social Networks, CASoN 201

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

The Dynamic Structural Patterns of Social Networks Based on Triad Transitions

In modern social networks built from the data collected in various computer systems we observe constant changes corresponding to external events or the evolution of underlying organizations. In this work we present a new approach to the description and quantifying evolutionary patterns of social networks illustrated with the data from the Enron email dataset. We propose the discovery of local network connection patterns (in this case: triads of nodes), measuring their transitions during network evolution and present the preliminary results of this approach. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads, then we show how it can help to discover the dynamic patterns of network evolution. Also, we analyse the roles performed by different triads in the network evolution by the creation of triad transition graph built from the TTM, which allows us to characterize the tendencies of structural changes in the investigated network. The future application

Temporal Changes in Local Topology of an Email-Based Social Network

The dynamics of complex social networks has become one of the research areas of growing importance. The knowledge about temporal changes of the network topology and characteristics is crucial in networked communication systems in which accurate predictions are important. The local network topology can be described by the means of network motifs which are small subgraphs -- usually containing from 3 to 7 nodes. They were shown to be useful for creating profiles that reveal several properties of the network. In this paper, the time-varying characteristics of social networks, such as the number of nodes and edges as well as clustering coefficients and different centrality measures are investigated. At the same time, the analysis of three-node motifs (triads) was used to track the temporal changes in the structure of a large social network derived from e-mail communication between university employees. We have shown that temporal changes in local connection patterns of the social network are indeed correlated with the changes in the clustering coefficient as well as various centrality measures values and are detectable by means of motifs analysis. Together with robust sampling network motifs can provide an appealing way to monitor and assess temporal changes in large social networks

Predicting the Evolution of Social Networks: Optimal Time Window Size for Increased Accuracy

This study investigates the data preparation process for predictive modelling of the evolution of complex networked systems, using an e–mail based social network as an example. In particular, we focus on the selection of optimal time window size for building a time series of network snapshots, which forms the input of chosen predictive models. We formulate this issue as a constrained multi–objective optimization problem, where the constraints are specific to a particular application and predictive algorithm used. The optimization process is guided by the proposed Windows Incoherence Measures, defined as averaged Jensen-Shannon divergences between distributions of a range of network characteristics for the individual time windows and the network covering the whole considered period of time. The experiments demonstrate that the informed choice of window size according to the proposed approach allows to boost the prediction accuracy of all examined prediction algorithms, and can also be used for optimally defining the prediction problems if some flexibility in their definition is allowed

Deontic Logic-based Framework for Ontology Aligment in Agent Communities

In this paper we consider a multiagent system with multiple ontologies. The agents maintain the ontologies individually which leads to frequent changes and possible knowledge inconsistencies. We propose a general framework for decision making about ontology alignment and negotiation which takes into account the properties of the actual communication network and utilizes the Deontic Logic formalism for reasoning