scispace - formally typeset
Search or ask a question
Author

Eytan Adar

Other affiliations: PARC, University of Washington, Xerox  ...read more
Bio: Eytan Adar is an academic researcher from University of Michigan. The author has contributed to research in topics: Computer science & Web page. The author has an hindex of 50, co-authored 127 publications receiving 12856 citations. Previous affiliations of Eytan Adar include PARC & University of Washington.


Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, the authors show that some factors are better indicators of social connections than others, and that these indicators vary between user populations, and provide potential applications in automatically inferring real world connections and discovering, labeling, and characterizing communities.

2,578 citations

Journal ArticleDOI
TL;DR: It is argued that free riding leads to degradation of the system performance and adds vulnerability to the system, and copyright issues might become moot compared to the possible collapse of such systems.
Abstract: An extensive analysis of user traffic on Gnutella shows a significant amount of free riding in the system. By sampling messages on the Gnutella network over a 24-hour period, we established that almost 70% of Gnutella users share no files, and nearly 50% of all responses are returned by the top 1% of sharing hosts. Furthermore, we found out that free riding is distributed evenly between domains, so that no one group contributes significantly more than others, and that peers that volunteer to share files are not necessarily those who have desirable ones. We argue that free riding leads to degradation of the system performance and adds vulnerability to the system. If this trend continues copyright issues might become moot compared to the possible collapse of such systems.

1,725 citations

Posted Content
TL;DR: In this paper, small world search strategies using a contact's position in physical space or in an organizational hierarchy relative to the target can effectively be used to locate most individuals in a social network.
Abstract: We address the question of how participants in a small world experiment are able to find short paths in a social network using only local information about their immediate contacts. We simulate such experiments on a network of actual email contacts within an organization as well as on a student social networking website. On the email network we find that small world search strategies using a contact's position in physical space or in an organizational hierarchy relative to the target can effectively be used to locate most individuals. However, we find that in the online student network, where the data is incomplete and hierarchical structures are not well defined, local search strategies are less effective. We compare our findings to recent theoretical hypotheses about underlying social structure that would enable these simple search strategies to succeed and discuss the implications to social software design.

588 citations

Journal ArticleDOI
TL;DR: It is found that small world search strategies using a contact’s position in physical space or in an organizational hierarchy relative to the target can effectively be used to locate most individuals, but in the online student network, where the data is incomplete and hierarchical structures are not well defined, local search strategies are less effective.

577 citations

Proceedings ArticleDOI
19 Sep 2005
TL;DR: This paper describes a novel inference scheme that takes advantage of data describing historical, repeating patterns of "infection" to track information flow in blogspace, as well as a visualization system that allows for the graphical tracking of information flow.
Abstract: Beyond serving as online diaries, weblogs have evolved into a complex social structure, one which is in many ways ideal for the study of the propagation of information. As weblog authors discover and republish information, we are able to use the existing link structure of blogspace to track its flow. Where the path by which it spreads is ambiguous, we utilize a novel inference scheme that takes advantage of data describing historical, repeating patterns of "infection." Our paper describes this technique as well as a visualization system that allows for the graphical tracking of information flow.

470 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

17,647 citations

Journal ArticleDOI
TL;DR: This publication contains reprint articles for which IEEE does not hold copyright and which are likely to be copyrighted.
Abstract: Social network sites SNSs are increasingly attracting the attention of academic and industry researchers intrigued by their affordances and reach This special theme section of the Journal of Computer-Mediated Communication brings together scholarship on these emergent phenomena In this introductory article, we describe features of SNSs and propose a comprehensive definition We then present one perspective on the history of such sites, discussing key changes and developments After briefly summarizing existing scholarship concerning SNSs, we discuss the articles in this special section and conclude with considerations for future research

14,912 citations

Journal ArticleDOI
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Abstract: We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.

12,882 citations

Proceedings Article
19 Mar 2009
TL;DR: This work presents several key features of Gephi in the context of interactive exploration and interpretation of networks, and highlights key aspects of dynamic network visualization.
Abstract: Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, filtering, navigating, manipulating and clustering. Finally, by presenting dynamic features of Gephi, we highlight key aspects of dynamic network visualization.

7,917 citations

Proceedings ArticleDOI
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

7,116 citations