180,248 research outputs found

    Predictive trend mining for social network analysis

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    This thesis describes research work within the theme of trend mining as applied to social network data. Trend mining is a type of temporal data mining that provides observation into how information changes over time. In the context of the work described in this thesis the focus is on how information contained in social networks changes with time. The work described proposes a number of data mining based techniques directed at mechanisms to not only detect change, but also support the analysis of change, with respect to social network data. To this end a trend mining framework is proposed to act as a vehicle for evaluating the ideas presented in this thesis. The framework is called the Predictive Trend Mining Framework (PTMF). It is designed to support "end-to-end" social network trend mining and analysis. The work described in this thesis is divided into two elements: Frequent Pattern Trend Analysis (FPTA) and Prediction Modeling (PM). For evaluation purposes three social network datasets have been considered: Great Britain Cattle Movement, Deeside Insurance and Malaysian Armed Forces Logistic Cargo. The evaluation indicates that a sound mechanism for identifying and analysing trends, and for using this trend knowledge for prediction purposes, has been established

    Exploring the Evolution of Node Neighborhoods in Dynamic Networks

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    Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes

    Couples’ places of meeting in late 20th century Britain: class, continuity and change

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    This article examines couples’ places or contexts of meeting in the second half of the 20th century in Great Britain, utilizing a typology developed by Bozon and Héran. The continuities are as striking as the changes, with social networks maintaining a consistent level of importance, but with trends towards meeting at places of education and work, and away from meeting in public places for drinking, eating or socializing. Rather than reflecting the impact of the rise of individualism and self-identity, these trends arguably reflect the changing importance of settings within people's daily lives, as may the recent growth in internet dating. Social class appears to have become more strongly related to the likelihood of meeting in ‘public’ settings, apparently more common in Britain than elsewhere. Achieved characteristics, especially occupational class, have a greater impact than parental class. Variations between place of meeting categories in the extent of occupational class homogamy appear to reflect levels of class homogeneity within settings more than the impact of either individualism or a homogamy norm. Regional variations in places of meeting highlight the ongoing importance of structural factors such as patterns of sociability or cultural norms

    Mesoscopic structure and social aspects of human mobility

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    The individual movements of large numbers of people are important in many contexts, from urban planning to disease spreading. Datasets that capture human mobility are now available and many interesting features have been discovered, including the ultra-slow spatial growth of individual mobility. However, the detailed substructures and spatiotemporal flows of mobility - the sets and sequences of visited locations - have not been well studied. We show that individual mobility is dominated by small groups of frequently visited, dynamically close locations, forming primary "habitats" capturing typical daily activity, along with subsidiary habitats representing additional travel. These habitats do not correspond to typical contexts such as home or work. The temporal evolution of mobility within habitats, which constitutes most motion, is universal across habitats and exhibits scaling patterns both distinct from all previous observations and unpredicted by current models. The delay to enter subsidiary habitats is a primary factor in the spatiotemporal growth of human travel. Interestingly, habitats correlate with non-mobility dynamics such as communication activity, implying that habitats may influence processes such as information spreading and revealing new connections between human mobility and social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table (supporting information

    Detecting Blackholes and Volcanoes in Directed Networks

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    In this paper, we formulate a novel problem for finding blackhole and volcano patterns in a large directed graph. Specifically, a blackhole pattern is a group which is made of a set of nodes in a way such that there are only inlinks to this group from the rest nodes in the graph. In contrast, a volcano pattern is a group which only has outlinks to the rest nodes in the graph. Both patterns can be observed in real world. For instance, in a trading network, a blackhole pattern may represent a group of traders who are manipulating the market. In the paper, we first prove that the blackhole mining problem is a dual problem of finding volcanoes. Therefore, we focus on finding the blackhole patterns. Along this line, we design two pruning schemes to guide the blackhole finding process. In the first pruning scheme, we strategically prune the search space based on a set of pattern-size-independent pruning rules and develop an iBlackhole algorithm. The second pruning scheme follows a divide-and-conquer strategy to further exploit the pruning results from the first pruning scheme. Indeed, a target directed graphs can be divided into several disconnected subgraphs by the first pruning scheme, and thus the blackhole finding can be conducted in each disconnected subgraph rather than in a large graph. Based on these two pruning schemes, we also develop an iBlackhole-DC algorithm. Finally, experimental results on real-world data show that the iBlackhole-DC algorithm can be several orders of magnitude faster than the iBlackhole algorithm, which has a huge computational advantage over a brute-force method.Comment: 18 page

    A rule dynamics approach to event detection in Twitter with its application to sports and politics

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    The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events

    Can environmental governance benefit from an ICT-social capital nexus in civil society?

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    Although the potential of Information and Communication Technologies (ICT) to foster social capital in civil society has been duly acknowledged, few studies have empirically explored the ICT-social capital nexus in the context of community organizations. Huysman and Wulf (2004) consider the lack of interest in the area of ‘ICT and social capital’ as worrisome in today’s increasingly network-centric society. Since the prospect of ICT furthering social capital is simply too significant to ignore, this paper responds to this gap by reporting on one aspect of a 2008 survey of environmental community organizations (ECOs) undertaken to develop a broader understanding of the linkages between organizational social capital and information and communication technologies in the Perth region of Western Australia. By exploring the trend of ICT uptake, pattern of intra-organizational as well as inter-organizational interactions, and the association between ICT uptake and organizational interactions, this paper critically engages in the ‘ICT and social capital’ debate and discusses the implications of ICT-social capital nexus in the context of environmental governance
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