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Computational Aspects of Social Networks, 2009. CASON '09. International Conference on

Date 24-27 June 2009

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Displaying Results 1 - 25 of 35
  • [Front cover]

    Page(s): C1
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    Freely Available from IEEE
  • [Title page i]

    Page(s): i
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    Freely Available from IEEE
  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - vii
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  • Message from Chairs

    Page(s): viii - ix
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    Freely Available from IEEE
  • Organizing Committee

    Page(s): x - xi
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    Freely Available from IEEE
  • International Scientific Committee/Reviewers

    Page(s): xii
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    Freely Available from IEEE
  • Social Influence Models Based on Starbucks Networks

    Page(s): 3 - 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3725 KB) |  | HTML iconHTML  

    Starbucks coffee shops have been spread rapidly and widely all over the world, which implies that there may be diffusive powers among them and thus can be represented as social networks. In particular, the spreading speed of Starbuck Korea was at record levels. In this paper, we constructed social networks using the information about Starbuck Korea (ex. latitude and longitude of each Starbucks store in Korea, the opening date of them, opening orders of them, etc.) and evaluated influence scores of each store to measure the spreading power of Starbucks in Korea. Here, we proposed two network evaluation models, Dynamic Influence Model and Static Influence Model. Through these models, we can represent location based social networks and evaluate each node's diffusive power for expanding the size of networks and for spreading coverage all over the network. View full abstract»

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  • A Random Network Generator with Finely Tunable Clustering Coefficient for Small-World Social Networks

    Page(s): 10 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (720 KB) |  | HTML iconHTML  

    Many social networks share two generic distinct features: power law distributions of degrees and a high clustering. In some cases, it is difficult to obtain the structure information of real networks. Network generators provide a way to generate test networks for simulation. We present a random network generator to generate test networks with prescribed power law distributions of degrees and a finely tunable average clustering coefficient. The generator is composed of three steps. First, the degree sequences are generated following the given degree power law exponents. Second, the generator constructs a test network with these degree sequences. Third, the test network is modified to meet the prescribed average clustering coefficient as closely as possible. Experiments show the impact of the clustering coefficient on network connectivity using this generator. The comparison with existing random network generators is presented. View full abstract»

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  • Using a Matrix Decomposition for Clustering Data

    Page(s): 18 - 23
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (211 KB) |  | HTML iconHTML  

    There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Matrix Decomposition (Singular Value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF)) can be good solution for the search results clustering. View full abstract»

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  • A Performance of Centrality Calculation in Social Networks

    Page(s): 24 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (595 KB) |  | HTML iconHTML  

    To analyze large social networks a lot of effort and resources are usually required. Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network characteristics. One of them is node position, which can be used to assess the importance of a given node within either the whole social network or the smaller subgroup. Three algorithms that can be utilized in the process of node position evaluation are presented in the paper: PIN edges, PIN nodes, and PIN hybrid. Also, different algorithms for indegree and outdegree prestige measures have been developed and tested. According to the experiments performed, the algorithms based on processing of edges are always faster than the others. View full abstract»

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  • Extended Generalized Blockmodeling for Compound Communities and External Actors

    Page(s): 32 - 39
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (987 KB) |  | HTML iconHTML  

    Some social communities evident their own unique internal structure. In the paper we consider social communities composed of several cohesive subgroups which we call compound communities. For such communities, an extended generalized blockmodeling is proposed, taking into account the structure of compound communities and relations with external actors. Using the extension, the community protection approach is proposed and used in detection of spam directed towards an e-mail local society. View full abstract»

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  • Enriching Trust Prediction Model in Social Network with User Rating Similarity

    Page(s): 40 - 47
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (499 KB) |  | HTML iconHTML  

    Trust management is an increasingly important issue in large social networks, where the amount of data is too extensive to be analysed by ordinary users. Hence there is an urgent need for research aiming at building automated systems that can support users in making their decisions concerning trust. This work is a preliminary implementation of selected ideas described in our previous research proposal which concerns taking a machine learning approach to the problem of trust prediction in social networks.We report experiments conducted on a publicly available social network dataset epinions.com. The results indicate that i) it is possible to predict trust to some extent, but much room for improvement is present; ii) enriching the model with attributes based on similarity between users can significantly improve trust prediction accuracy for more similar users. View full abstract»

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  • On the Synergies between Online Social Networking, Face Recognition and Interactive Robotics

    Page(s): 48 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (451 KB) |  | HTML iconHTML  

    This paper explores the intersection of three areas: interactive robots, face recognition, and online social networks, by presenting and discussing an implemented real-world system that combines all three, a ldquoFaceBotsrdquo robot. Our robot is a mobile robot with face recognition, natural language dialogue, as well as mapping capabilities. The robot is also equipped with a social database containing information about the people it interacts with, and is also connected in real-time to the ldquoFacebookrdquo online social networking Web site, which contains information as well as partially tagged pictures. Our system demonstrates the benefits of this triangle of interconnection: it is not only the case that facebook information can lead to more interesting interactions, but also that: facebook photos enable better face recognition, interactive robots enable robot-mediated publishing of photos and information on facebook. Most importantly, as we shall see in detail, social information enables significantly better and faster face recognition, as an interesting bidirectional relationship exists between the ldquofriendsrdquo relation in social networks and the ldquofaces appear in the same picturerdquo relation in face recognition. We will present algorithms for exploiting this relationship, as well as quantitative results. The two main novelties of our system are: this is the first interactive conversational mobile robot that utilizes and publishes social information in facebook, and is also the first system utilizing the social context of conjectured identities in a photo for better face recognition. View full abstract»

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  • Social Network - An Autonomous System Designed for Radio Recommendation

    Page(s): 57 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1112 KB) |  | HTML iconHTML  

    This paper describes the functions of a system proposed for the music tube recommendation from social network data base. Such a system enables the automatic collection, evaluation and rating of music critics, the possibility to rate music tube by auditors and the recommendation of tubes depended from auditor's profiles in form of regional internet radio. First, the system searches and retrieves probable music reviews from the Internet. Subsequently, the system carries out an evaluation and rating of those reviews. From this list of music tubes the system directly allows notation from our application. Finally the system automatically create the record list diffused each day depended form the region, the year season, day hours and age of listeners. Our system uses linguistics and statistic methods for classifying music opinions and data mining techniques for recommendation part needed for recorded list creation. The principal task is the creation of popular intelligent radio adaptive on auditor's age and region - IA-Regional-Radio. View full abstract»

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  • A Space-Based Layout Algorithm for the Drawing of Co-citation Networks

    Page(s): 65 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (585 KB) |  | HTML iconHTML  

    We present in this paper a drawing algorithm to represent graphically co-citation networks (scientograms). These networks have some interesting and unusual topological properties which are often valuable to be visualized. In general, these networks are pruned with a network scaling algorithm, then visualized using a drawing algorithm. However, typical drawing algorithms do not work properly, especially when the size of the networks grows. Edge crossings appear while the drawing space is not adequately filled resulting in an unsightly display. The approach presented in this paper is able to print the networks filling all the available space in an aesthetic way, while avoiding edge crossings. The algorithm is detailed and compared with the classical Kamada-Kawai drawing algorithm on two maps. View full abstract»

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  • Social Group Identification and Clustering

    Page(s): 73 - 79
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (595 KB) |  | HTML iconHTML  

    Some methods for object group identification applicable for social group identification are compared. We suppose that people are characterized by their actions, for example the deputies are characterized by their voting habits. We are interested in binary data analysis (e.g. the result of voting is yes or not). The dataset consisting of the roll-call votes records in the Russian parliament in 2004 was analyzed. Methods of hierarchical and fuzzy clustering, and Boolean factor analysis are applied. In the first case, we propose two-step analysis in which factor loadings (as result of factor analysis of objects) obtained in the first step are interpreted by cluster analysis in the second step. For the cluster number determination both traditional and modified coefficients are used. Further, we suggest using Hopfield-like neural network based Boolean factor analysis for this purpose. This proposed method gives the best results in the case of deputies grouping. View full abstract»

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  • Social Aspects of Web Page Contents

    Page(s): 80 - 87
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2405 KB) |  | HTML iconHTML  

    In this paper we try to consider a Web page as information with social aspects. Each Web page is the result of invisible social interaction. This interaction between different groups of people translates into a certain unification of Web page creation. External signs of this unification are the features of the Web page, that meets the userpsilas expectations. Through analysis of the features, we can obtain information that can simply describe the Web page. This simple description contains strong information about the social group the page is intended for. If the user uses this information to refine the search, then he identifies himself as a member of a certain social group. For the description of the social aspects of Web pages we use the term MicroGenre. This paper describes the fundamental concepts of MicroGenre and also illustrate experiments for the detection and usage of MicroGenres. View full abstract»

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  • Social Network Signatures: A Framework for Re-identification in Networked Data and Experimental Results

    Page(s): 88 - 97
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (482 KB) |  | HTML iconHTML  

    Data on large dynamic social networks, such as telecommunications networks and the Internet, are pervasive. However, few methods conducive to efficient large-scale analysis exist. In this paper, we focus on the task of re-identification. Re-identification in the context of dynamic networks is a matching problem that involves comparing the behavior of networked entities across two time periods. Prior research has reported success in the domains of e-mail alias detection, author attribution, and identifying fraudulent consumers in the telecommunications industry. In this work, we address the question of "why are we able to re-identify entities on real world dynamic networks?" Our contribution is two-fold. First, we address the challenge of scale with a framework for matching that does not require pairwise comparisons to ascertain the similarity scores between networked entities. Second, we show our method is robust against missing links but less tolerant to noise. Using our framework, we provide a performance estimate for re-identification on networks based solely on their degree distribution and dynamics. This work has significant implications for re-identification problems where scale is a challenge as well as for problems where false negatives (e.g., when fraudulent consumers are not labeled as fraudulent) cannot be observed. View full abstract»

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  • Review-Based Ranking of Wikipedia Articles

    Page(s): 98 - 104
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (920 KB) |  | HTML iconHTML  

    Wikipedia, the largest encyclopedia on the Web, is often seen as the most successful example of crowdsourcing. The encyclopedic knowledge it accumulated over the years is so large that one often uses search engines, to find information in it. In contrast to regular Web pages, Wikipedia is fairly structured, and articles are usually accompanied with history pages, categories and talk pages. The meta-data available in these pages can be analyzed to gain a better understanding of the content and quality of the articles. We discuss how the rich meta-data available in wiki pages can be used to provide better search results in Wikipedia. Built on the studies on "Wisdom of Crowds" and the effectiveness of the knowledge collected by a large number of people, we investigate the effect of incorporating the extent of review of an article in the quality of rankings of the search results. The extent of review is measured by the number of distinct editors contributed to the articles and is extracted by processing Wikipedia's history pages. We compare different ranking algorithms that explore combinations of text-relevancy, PageRank, and extent of review. The results show that the review-based ranking algorithm which combines the extent of review and text-relevancy outperforms the rest; it is more accurate and less computationally expensive compared to PageRank-based rankings. View full abstract»

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  • Detecting Communities in Large Networks by Iterative Local Expansion

    Page(s): 105 - 112
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (478 KB) |  | HTML iconHTML  

    Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure, which means that there exists densely connected groups of vertices, with only sparser connections between groups. Identifying community structure in networks has attracted much research attention. However, most existing approaches require structure information of the graph in question to be completely accessible, which is impractical for some large networks, e.g., the World Wide Web (WWW). In this paper, we propose a community discovery algorithm for large networks that iteratively finds communities based on local information only. We compare our algorithm with previous global approaches to show its scalability. Experimental results on real world networks, such as the co-purchase network from Amazon, verify the feasibility and effectiveness of our approach. View full abstract»

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  • An Architecture to Facilitate Membership and Service Management in Trusted Communities

    Page(s): 113 - 118
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (699 KB) |  | HTML iconHTML  

    Ubiquitous connectivity today allows many users to remain connected regardless of location with various kinds of communities. This paper studies challenges in building trusted communities that encompass both new users as well as users already possessing credentials from other well known connectivity providers, federations, content providers and social networks. We postulate that trusted communities are initially created as a means to access some services, but become enriched with user created services. We present an architecture aimed at managing the complexity of service composition, access as well as guarantees of authenticity. Since users possess multiple credentials from various identity providers, we address this in our architecture from the service access perspective. In addition, our model explicitly takes into account cases where users may temporarily be granted access to a communitypsilas services based on recommendations from existing members. View full abstract»

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  • The Graph Descriptors of E-content Unit Organisation and Controlling Features

    Page(s): 119 - 126
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (632 KB) |  | HTML iconHTML  

    The e-learning databases content description methods, for presentation and distribution, were introduced in many works (as in. IMS and Common Cartridge action), for standardisation (SCORM) and distribution platforms for authoring systems (MOODLE, MAMS). The personalization processes of training units structure finding is still an investigations area. The specifications like IMS LD supported by OUML languages allow the courses structure modelling, leaving not solved ontology of courseware and presentation standards. The contribution presents an approach allowing controlling the course construction by a directed multi-graph, supported by a current knowledge of the course user. The decisions are based on a fuzzy logic measures, describing the userpsilas knowledge. The authors elaborated the e-learning applications development platform (called multimedia applications management shell - MAMS) provided with various tools that simplify the e-learning unitpsilas development and the applications controlling processes implementation. View full abstract»

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  • Sentence Factorization for Opinion Feature Mining

    Page(s): 129 - 132
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (143 KB) |  | HTML iconHTML  

    Opinion mining has tremendous potentials in extracting valuable information and experience from individuals on products and services. In particular, product features extraction and sentiment scoring on extracted features are fundamental steps. Opinion knowledge extraction often involves extensive application of natural language processing, manual labeling and machine learning methods.In this paper, we focus on developing fine-grained product feature extractions with minimal tailor build language models and labeling.A threshold-normalized sentence-level word model is proposed for opinion feature mining. The opinion feature extraction is then solved via matrix factorization technique. Evaluation on feature-entropies, sentence-entropies and human evaluation demonstrated the superiority of our approach. Highly relevant and fine-grained opinion features are extracted automatically. View full abstract»

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