Social Network Analysis: Applications: Event Programme

This seminar explores a number of current uses and applications of network analysis, including applications in social movement analysis, criminology, socio-linguistics and the study of literary networks. The aim of this is both to facilitate cross-pollination between domains of application and to offer exemplars of the method in action for those new to this approach. We hope that this seminar will prove to be an interesting introduction to network analysis for those previously unacquainted with it, which will both inspire and equip them to participate in the later seminars

Social Network Analysis

{Excerpt} Power no longer resides exclusively (if at all) in states, institutions, or large corporations. It is located in the networks that structure society. Social network analysis seeks to understand networks and their participants and has two main focuses: the actors and the relationships between them in a specific social context. The information revolution has given birth to new economies structured around flows of data, information, and knowledge. In parallel, social networks have grown stronger as forms of organization of human activity. Social networks are nodes of individuals, groups, organizations, and related systems that tie in one or more types of interdependencies: these include shared values, visions, and ideas; social contacts; kinship; conflict; financial exchanges; trade; joint membership in organizations; and group participation in events, among numerous other aspects of human relationships. Indeed, it sometimes appears as though networked organizations out compete all other forms of organization—certainly, they outpace vertical, rigid, command-and-control bureaucracies. When they succeed, social networks influence larger social processes by accessing human, social, natural, physical, and financial capital, as well as the information and knowledgecontent of these. (In development work, they can impact policies, strategies, programs, and projects—including their design, implementation, and results—and the partnerships that often underpin these.) To date, however, we are still far from being able to construe their public and organizational power in ways that can harness their potential. Understanding when, why, and how they function best is important. Here, social network analysis can help

Compressive Network Analysis

Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets

Social Network Analysis Dalam Melihat Kecenderungan Pemberitaan Pada Akun Twitter “@Detikcom” Dan “@Metro_tv”

Revolusi digital yang terjadi saat ini telah mengubah perilaku orang dalam banyak hal, termasuk dalam memperoleh informasi atau berita. Twitter sebagai salah satu sosial media yang banyak digunakan, telah dimanfaatkan berbagai situs berita online sebagai sarana untuk menyampaikan beritanya termasuk oleh detik dengan akun @detikcom dan Metro tv dengan akun @Metro_TV. Makalah ini bertujuan untuk untuk mengetahui kecenderungan topik pemberitaan dan mengetahui kata yang paling sering muncul pada akun twitter berita @Detik.com dan @Metro_TV. Penelitian ini menggunakan 500 tweets terakhir yang berasal dari timeline akun twitter dari @Detik.com dan @Metro_TV. Analisis data menggunakan social network analysis berupa analisis text atau text mining dengan bantuan software R. Berdasarkan hasil analisis didapatkan bahwa kedua akun twitter tersebut mempunyai kecenderungan pemberitaan yang sama yaitu mengenai bencana, perbedaan hanya terdapat pada topik bencana yang dibahas yang dapat dilihat dari tiga terms teratas @detikcom yaitu gempa, banjir dan warga. Sedangkan untuk akun twitter @Metro_TV tiga terms teratas yaitu banjir, tewas dan akibat. Topik yang paling sering muncul pada pemberitaan @detikcom adalah gempa, sedangkan topik yang menjadi pemberitaan utama pada @Metro_TV adalah banjir. Plot network of terms memperlihatkan bahwa kata gempa pada @detikcom berhubungan erat dengan kebumen dan korban. Kata banjir pada @Metro_TV berhubungan erat dengan pantura, akibat dan ekonomi

Semantic Network Analysis of Ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size