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

A historical look using virtual microscopy: the first case report of adrenomyeloneuropathy (AMN)

The history of adrenoleukodystrophy (ALD), adrenomyeloneuropathy (AMN) and other peroxisomal diseases is exemplary for the stunning progress of scientific medicine within the past 50 years. Like many breakthroughs in medicine, the detailed analysis of patients’ pathologically affected tissues was instrumental, resulting in step-wise systematic clarification of what had remained enigmatic until the 1970s. This flashback paper is a recollection of the first neuropathological description of a slowly evolving clinical phenotype, spastic paraparesis with adrenal insufficiency, in a young adult by Budka et al. 1976 [3], using virtual microscopy of the original histologic slides. The clinico-pathological presentation derives from the classical cerebral ALD phenotype in boys, where electron microscopy demonstrated the underlying pathological hallmark of characteristic lipid inclusions shared by both phenotypes. Our report allowed the delineation of a new disease type almost simultaneously described in more cases as AMN by Griffin et al. 1977 [4] and Schaumburg et al. 1977 [11]. Moreover, our report indicated clinical heterogeneity in the ALD disease group that, as shown later, extends further to females, to Addison-only, and even to asymptomatic subjects. The gene underlying ALD was discovered in 1993 as a defect in the ABCD1 gene. Yet, it has hitherto remained unclear how the gene defect causes the strikingly broad and unpredictable phenotypic spectrum of ALD/AMN

Data stream synchronisation for defining meaningful fMRI classification problems

Application of machine learning techniques to the functional Magnetic Resonance Imaging (fMRI) data is recently an active field of research. There is however one area which does not receive due attention in the literature – preparation of the fMRI data for subsequent modelling. In this study we focus on the issue of synchronization of the stream of fMRI snapshots with the mental states of the subject, which is a form of smart filtering of the in- put data, performed prior to building a predictive model. We demonstrate, investigate and thoroughly discuss the negative effects of lack of alignment between the two streams and propose an original data-driven approach to efficiently address this problem. Our solution involves casting the issue as a constrained optimization problem in combination with an alternative classification accuracy assessment scheme, applicable to both batch and on-line scenarios and able to capture information distributed across a number of input samples lifting the common simplifying i.i.d. assumption. The proposed method is tested using real fMRI data and experimentally compared to the state-of-the-art ensemble models reported in the literature, outperforming them by a wide margin