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An application of community discovery in academical social networks
No full textArslan, Enis (Dogus Author)The objective of this thesis is to discover social communities in a social network using different social network community discovery methods that utilizes metrics and structures like degree, clustering coefficient, k-cores, weak and strong components. In this study we have used two different datasets: DBLP and Arxiv High-energy physics theory citation network. Two Social Network Analysis tools are used in this thesis: Pajek and Gephi. In order to use Pajek and Gephi, DBLP dataset is converted by developing a new conversion and refinement framework. After dataset conversion, we have used Pajek tool to discover communities by applying several clustering metrics to the social networks. Additionally, Gephi tool is used for supporting the analysis of discovering communities by using extended metrics. Gephi tool enables visualization of the results graphically and gives the reports of the analyses. At the end of the analyses, we have obtained several reports and graphs that show triads and skeleton structure of the communities in the networks. These reports and graphs give social communities and the leaders of networks and several characteristics of these communities.Bu tezin amacı, degree, clustering coefficient, k-cores, weak, strong components gibi çeşitli sosyal ağ topluluk ölçü ve yapılarını kullanarak bir sosyal ağ'daki sosyal toplulukların keşfedilmesidir. Bu çalışmada iki farklı veri seti kullanılmıştır: DBLP ve Arxiv High-energy physics theory citation ağı. Bu tezde iki Sosyal Ağ Analizi programı kullanılmıştır: Pajek ve Gephi. Pajek ve Gephi'yi kullanabilmek için yeni bir framework tasarlanarak, DBLP veri kümesi çeşitli rafine etme ve düzenleme işlemine tabi tutulmuştur. Veri kümesi düzenlemelerinden sonra, Pajek programı birçok kümeleme metriklerini sosyal ağ'lara uygulayarak toplulukları keşfetmek için kullanılmıştır. Bunlara ek olarak, Gephi programı ile ilave metrikleri kullanarak yapılan analiz desteklenmiştir. Gephi programı ile sonuçlar grafiksel olarak görselleştirilmiş ve analiz raporları hazırlanmıştır. Analizin sonunda, sosyal ağlardaki sosyal toplulukların üçlü topluluk ve iskelet yapılarını gösteren çeşitli rapor ve grafikler elde edilmiştir. Bu raporlar ve grafikler sosyal ağlardaki sosyal toplulukları ve liderlerini, ve birçok topluluk karakterini göstermektedir.PREFACE, i -- ABSTRACT, ii -- ÖZET, iii -- ACKNOWLEDGEMENTS, iv -- LIST OF TABLES, viii -- ABBREVIATIONS, ix -- 1. INTRODUCTION, 1 -- 2. STUDY OF NETWORKS, 3 -- 2.1. Network Theory, 3 -- 2.1.1. Paths, 5 -- 2.1.2. Components, 7 -- 2.1.3. Cores, 11 -- 2.1.4. Cliques, 12 -- 2.1.5. Plex, 13 -- 2.2. Measures and Metrics, 13 -- 2.2.1. Degree and Centrality, 13 -- 2.2.2. Betweenness Centrality, 14 -- 2.2.3. Closeness Centrality, 15 -- 2.2.4. Katz Centrality, 15 -- 2.2.5. Tie Strength, 16 -- 2.2.6. Triadic Closure, 16 -- 2.2.7. Clustering Coefficient, 17 -- 2.2.8. Embeddedness, 17 -- 2.2.9. Transitivity, 18 -- 2.2.10. Homophily, 18 -- 3. SOCIAL NETWORK ANALYSIS, 19 -- 3.1. Social Networks, 19 -- 3.2. Community Discovery & Graph Partitioning Algorithms, 23 -- 3.2.1. A listof Community Discovery Algorithms, 23 -- 3.2.2. Some of the commonly used Community Discovery Algorithms, 29 -- 3.2.2.1. Kemighan Lin (KL) Algorithm, 29 -- 3.2.2.2. Spectral Partitioning Algorithms, 31 -- 3.2.2.3. Newman's Edge Betweenness Algorithm, 32 -- 3.2.2.4. Markov Clustering Algorithm (MCL), 34 -- 3.2.2.5. Hierachical Clustering Algorithm, 36 -- 3.2.2.6. K-core Community Discovery Method, 42 -- 3.2.2.7. Main Path Analysis Method, 44 -- 3.3. Tools for Social Network Analysis, 46 -- 3.3.1. Tools in General, 46 -- 3.3.2. Pajek, 47 -- 3.3.3. Gephi, 47 -- 3.3.3.1. Applications of Gephi, 48 -- 3.3.3.2. Underlying Technology, 48 -- 4. AN APPLICATION OF COMMUNITY DISCOVERY IN SOCIAL NETWORKS, 49 -- 4.1. K core Community Discovery Process, 49 -- 4.2. Data Sets, 49 -- 4.2.1. DBLP, 50 -- 4.2.2. Arxiv high energy physics theory citation network, 50 -- 4.3. Data Preprocessing and Conversion, 51 -- 4.3.1. Requirements for Data Preprocessing and Conversion, 51 -- 4.3.2. Data Preprocessing Phases, 52 -- 4.4. Discovering Comrnunities in the Dataset, 55 -- 4.4.1. Characteristics of Datasets, 56 -- 4.4.2. Analysis of DBLP Dataset, 56 -- 4.4.3. Analysis of Arxiv Dataset, 64 -- 5. CONCLUSION, 69 -- REFERENCES, 70 -- APPENDIX I. NET PAJEK NETWORK FILE SAMPLE, 72 -- APPENDIX II. C++ CODE OF DATASET REFINEMENT, 74 -- APPENDIX III. KEYWORDS OF THE MAIN PATH ARTICLES, 76 -- CURRICULUM VITAE, 8
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Graf Merkezilik Algoritmalarının Anlamsal Mesafe için Karşılaştırılmaları
No full textArslan, Enis ; Orhan, Umut, (Çukurova Üniversitesi Mühendislik Fakültesi Bilgisayar Mühendisliği Bölümü) ; Turan, Erhan, (Osmaniye Korkut Ata Üniversitesi Mühendislik Fakültesi Bilgisayar Mühendisliği Bölümü) ; Tülü, Çağatay, (Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi Bilgi İşlem Daire Başkanlığı)Semantic networks are kind of datasets used for natural language processing (NLP). Distance measurement for semantic networks, which are generally based on a graph structure, is a vital requirement for semantic analysis on concepts. Centrality measures can be used for calculating the semantic distance between concepts in a semantic network. In this paper, we evaluated graph centrality algorithms including PageRank, Hyperlink-induced Topic Search (HITS), and Betweenness Centrality on a semantic network, which was created from a Turkish dictionary dataset. Centrality measures special to these algorithms are used to calculate the semantic distance between synonym pairs in the semantic network. Also, we have used a simple centrality method beside the other three popular centrality algorithms to find out the most accurate and cost-effective method on our semantic network. Working on a bipartite model of the network which increases the complexity of implementation for centrality algorithms and performing calculations on a semantic network, that can be expanded with new nodes and edges, are two major challenges to overcome. Considering all these conditions, results from each algorithm are compared to pick out an optimal method for the semantic network.Anlamsal ağlar, doğal dil işleme (DDİ) için kullanılan graf tabanlı veri kümeleridir. Anlamsal ağlarda mesafe ölçümü ise, kavramların ağ içinde ilişkiler ile birbirine bağlılığının anlamsal analizi için çok önemli bir yere sahiptir. Bağlantılılık ölçümleriyle elde edilen değerler, anlamsal ağlardaki kavramlar arasındaki mesafe hesaplamaları için kullanılabilinir. Bu çalışmada, PageRank, Hyperlink-induced Topic Search (HITS) ve Arasındalık Merkeziliği graf bağlantılılık algoritmaları, Türkçe sözlükteki kavramlardan oluşturulan anlamsal ağ üzerinde uygulanmış ve elde edilen değerler ile anlamsal ağdaki eş anlamlı sözcükler arasındaki mesafe hesaplanmıştır. Bu üç önemli graf bağlantılılık algoritmaları, bu çalışmada kullanılan anlamsal ağ için tasarlanmış olan temel bir bağlantılılık yöntemiyle karşılaştırılmıştır. İki parçalı graf tasarımı ile oluşturulmuş olan Türkçe Sözlük anlamsal ağı üzerinde geleneksel graf bağlantılılık algoritmalarının uygulanması daha karmaşık hale gelmektedir. Uygulama esnasında gereken işleme zamanını arttırması, ayrıca ağa eklenecek olan yeni kavramlar ve bağlantılar sonrası ağın tekrar anlamsal mesafe için hesaplamalara ihtiyaç duyması, bağlantılılık algoritmalarının karşılaştığı iki önemli sorundur. Bu zorluklar ve anlamsal ağın iki parçalı graf yapısı göz önüne alındığında, her bir algoritma ile elde edilen sonuçlar karşılaştırılmış ve tasarlanan anlamsal ağ için en verimli yöntem bulunmaya çalışılmıştır
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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