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Visualization and Analysis of Email Networks
"... This paper presents various methods for visualization and analysis of email networks; visualization on the surface of a sphere to reveal communication patterns between different groups, a hierarchical drawing displaying the centrality analysis of nodes to emphasize important nodes, a 2.5D visualizat ..."
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This paper presents various methods for visualization and analysis of email networks; visualization on the surface of a sphere to reveal communication patterns between different groups, a hierarchical drawing displaying the centrality analysis of nodes to emphasize important nodes, a 2.5D visualization for temporal email networks to analyze the evolution of email relationships changing over time, and an ambient display for finding social circles derived from the email network. Each method was evaluated with various data sets from a research organization. We also extended our method for visual analysis of an email virus network.
Automated Social Network Analysis for Collaborative Work 1
"... Abstract. Inter-networked computers enable virtual collaborative work. In the course of interacting with one another, individuals send and receive messages and files of various sorts. This may be done within specialized collaborative work environments, or by simply employing a combination of differe ..."
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Cited by 4 (2 self)
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Abstract. Inter-networked computers enable virtual collaborative work. In the course of interacting with one another, individuals send and receive messages and files of various sorts. This may be done within specialized collaborative work environments, or by simply employing a combination of different communication tools and applications. In the course of doing their work, collaborators perform different actions that create and/or otherwise manipulate digital artifacts that are related to different aspects of their collaboration. Social network analysis is used to develop a fuller understanding of interactions between people. We describe a software prototype of a tool that automatically measures and analyzes aspects of collaboration developing visualizations of likely social interactions. In this paper we describe the system, some early results, and several different possible applications of the technology. 1
Adding taxonomies obtained by content clustering to semantic social network analysis
- On Collective Intelligence, Advances in Intelligent and Soft Computing
"... Abstract. This paper introduces a novel method to analyze the con-tent of communication in social networks. Content clustering methods are used to extract a taxonomy of concepts from each analyzed com-munication archive. Those taxonomies are hierarchical categorizations of the concepts discussed in ..."
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Cited by 1 (0 self)
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Abstract. This paper introduces a novel method to analyze the con-tent of communication in social networks. Content clustering methods are used to extract a taxonomy of concepts from each analyzed com-munication archive. Those taxonomies are hierarchical categorizations of the concepts discussed in the analyzed communication archives. Con-cepts are based on terms extracted from the communication’s content. The resulting taxonomy provides insights into the communication not possible through conventional social network analysis. 1
Coolhunting for Trends on the Web
"... This paper introduces a new way of measuring the popularity of brand names and famous people such as movie stars, politicians, and business executives. It is based upon the premise that in today’s Internet economy the Web displays a mirror of the real world. Our system uses TeCFlow, a social network ..."
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This paper introduces a new way of measuring the popularity of brand names and famous people such as movie stars, politicians, and business executives. It is based upon the premise that in today’s Internet economy the Web displays a mirror of the real world. Our system uses TeCFlow, a social networking tool developed for the last four years at MIT, to measure popularity and influence of brands and stars by looking at their relative position on the Web. It is based on the simple insight: “You are who links to you”. It applies the Social Network Analysis (SNA) metric of “betweenness centrality ” to the Web, looking at the linking structure of Web sites to find how Web pages discussing brands and stars are connected. It uses high-betweenness Web sites returned to a search engine query for a brand or star name as a proxy for the significance of this brand or star.
Copyright © 200x Inderscience Enterprises Ltd. Location Matters – Measuring the Efficiency of Business Social Networking
"... Abstract: This paper contributes to measuring the efficiency of business social networking events and organizations. In particular, we analyzed the communication at SHARE, an organization whose mission is to foster collaboration networks between the scientific and entrepreneurial communities of Swit ..."
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Abstract: This paper contributes to measuring the efficiency of business social networking events and organizations. In particular, we analyzed the communication at SHARE, an organization whose mission is to foster collaboration networks between the scientific and entrepreneurial communities of Switzerland and the greater Boston area. The study consists of two parts. In the first part, SHARE’s social network growth over more than a year was measured through an analysis of its e-mail traffic. In the second part, growth of social networks of individuals participating in a set of networking events during a collaboration program over one week was measured through a web-survey. Comparing individual social network growth through attendance and individual follow-up at events organized in Boston and San Francisco demonstrated creation of a much denser network in Boston – with an almost even split between academic and industrial participants in Boston, while the majority of participants in the Silicon Valley came from industry. Boston’s academic participants acted as information brokers, building bridges between industrial participants from Boston and Switzerland.
PROCEEDINGS, COINs13 COINS CHANGE LEADERS – LESSONS LEARNED FROM A DISTRIBUTED COURSE
"... In this paper we analyze the communication network of 50 students from five universities in three countries participating in a joint course on Collaborative Innovation Networks (COINs). Students formed ten teams. Interaction variables calculated from the e-mail archive of individual team members pre ..."
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In this paper we analyze the communication network of 50 students from five universities in three countries participating in a joint course on Collaborative Innovation Networks (COINs). Students formed ten teams. Interaction variables calculated from the e-mail archive of individual team members predict the level of creativity of the team. Oscillating leadership, where members switch between central and peripheral roles is the best predictor of creativity, it is complemented by the variance in the amount of sending or receiving information, and by answering quickly, and positive language. We verify our automatically generated creativity metrics with interviews.
To
, 2008
"... I am eternally grateful to my research supervisor, Prof. N. Balakrishnan for his invigorating guidance and valuable suggestions during the course of my research work. I thank him for encouraging my ideas and very patiently correcting my mistakes. I am also indebted to him for his utmost support, enc ..."
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I am eternally grateful to my research supervisor, Prof. N. Balakrishnan for his invigorating guidance and valuable suggestions during the course of my research work. I thank him for encouraging my ideas and very patiently correcting my mistakes. I am also indebted to him for his utmost support, encouragement and inspiration throughout the period of this work. I am thankful to him for always making time for me through his hectic schedule. My special thanks to my grandfather Prof. M. S. Murthy and my aunt Dr. Y. V. S. Lakshmi for being a great source of inspiration throughout my life. I also thank my grandmother Mrs. M. Sunanda and my uncle Mr. Y. K. Viswanath for always being there for me.
Analyzing the Creative Editing Behavior of Wikipedia Editors Through Dynamic Social Network Analysis
"... Elsevier use only: Received date here; revised date here; accepted date here This paper analyzes editing patterns of Wikipedia contributors using dynamic social network analysis. We have developed a tool that converts the edit flow among contributors into a temporal social network. We are using this ..."
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Elsevier use only: Received date here; revised date here; accepted date here This paper analyzes editing patterns of Wikipedia contributors using dynamic social network analysis. We have developed a tool that converts the edit flow among contributors into a temporal social network. We are using this approach to identify the most creative Wikipedia editors among the few thousand contributors who make most of the edits amid the millions of active
