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.
{"title":"Review-Based Ranking of Wikipedia Articles","authors":"Y. Ganjisaffar, S. Javanmardi, C. Lopes","doi":"10.1109/CASON.2009.14","DOIUrl":"https://doi.org/10.1109/CASON.2009.14","url":null,"abstract":"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.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114872876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces the Windmill method for constructing situation sensitive communication support systems for organizations consisting of a network of autonomous professionals involved in standard duties encountering occasional incidents of a time-critical nature for which they have to call for help. The Windmill method is based on statistical data filtering techniques for ranking available resources to handle incident according to their availability, location, skills and experience. It is especially useful for domains in which the human workforce changes over time and incidents are relatively sparse with respect to location and frequency of occurrence.
{"title":"The Windmill Method for Setting up Support for Resolving Sparse Incidents in Communication Networks","authors":"D. Ferro, C. Jonker, A. Salden","doi":"10.1109/CASoN.2009.17","DOIUrl":"https://doi.org/10.1109/CASoN.2009.17","url":null,"abstract":"This paper introduces the Windmill method for constructing situation sensitive communication support systems for organizations consisting of a network of autonomous professionals involved in standard duties encountering occasional incidents of a time-critical nature for which they have to call for help. The Windmill method is based on statistical data filtering techniques for ranking available resources to handle incident according to their availability, location, skills and experience. It is especially useful for domains in which the human workforce changes over time and incidents are relatively sparse with respect to location and frequency of occurrence.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116465666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Social Influence Models Based on Starbucks Networks","authors":"Minkyoung Kim, Byoung-Tak Zhang, June-Sup Lee","doi":"10.1109/CASoN.2009.26","DOIUrl":"https://doi.org/10.1109/CASoN.2009.26","url":null,"abstract":"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.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125371820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Piotr Bródka, Katarzyna Musial, Przemyslaw Kazienko
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.
{"title":"A Performance of Centrality Calculation in Social Networks","authors":"Piotr Bródka, Katarzyna Musial, Przemyslaw Kazienko","doi":"10.1109/CASoN.2009.20","DOIUrl":"https://doi.org/10.1109/CASoN.2009.20","url":null,"abstract":"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.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129559342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Social Network Signatures: A Framework for Re-identification in Networked Data and Experimental Results","authors":"Shawndra Hill, A. Nagle","doi":"10.2139/ssrn.1341394","DOIUrl":"https://doi.org/10.2139/ssrn.1341394","url":null,"abstract":"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.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129619430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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