BibSonomy publications for /user/jaeschke/networkWed Dec 21 08:52:42 CET 2011Proceedings of the National Academy of Sciences238577--8582Modularity and community structure in networks1032006clustering community graph modularity network structure Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.Thu Dec 01 08:13:48 CET 2011Making Sense of Microposts {(\#MSM2011)}may1--12Citation Analysis in Twitter: Approaches for Defining and Measuring Information Flows within Tweets during Scientific Conferences2011analysis citation microblogging network twitter This paper investigates Twitter usage in scientific contexts, particularly the use of Twitter during scientific conferences. It proposes a methodology for capturing and analyzing citations/references in Twitter. First results are presented based on the analysis of tweets gathered for two conference hashtags.Thu Nov 24 11:53:21 CET 2011cs.CGjulGMap: Drawing Graphs as MapsarXiv:0907.2585v12009citation drawing graph graphics graphviz sna analysis network social Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through dimensionality reduction techniques. However, these traditional methods often do not capture well the underlying structural information, clustering, and neighborhoods. In this paper, we describe GMap: a practical tool for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains All the maps referenced in this paper can be found in http://www.research.att.com/~yifanhu/GMap Wed Nov 16 12:18:29 CET 2011Proceedings of the National Academy of Sciences2404--409The structure of scientific collaboration networks982001analysis citation network science The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE biomedical research, the Los Alamos e-Print Archive physics, and NCSTRL computer science. I show that these collaboration networks form ” small worlds,” in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.Wed Oct 05 13:35:26 CEST 2011Proceedings of the National Academy of Sciences of the United States of America113747--3752The architecture of complex weighted networks1012004complex sna analysis network social Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.Tue Oct 04 10:23:38 CEST 2011Physical Review Emar3036127Modeling bursts and heavy tails in human dynamics732006dynamics sna analysis network social The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behavior into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. Here we provide direct evidence that for five human activity patterns, such as email and letter based communications, web browsing, library visits and stock trading, the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. We show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times. In contrast, priority blind execution is well approximated by uniform interevent statistics. We discuss two queuing models that capture human activity. The first model assumes that there are no limitations on the number of tasks an individual can hadle at any time, predicting that the waiting time of the individual tasks follow a heavy tailed distribution P(τw)∼τw−α with α=3∕2. The second model imposes limitations on the queue length, resulting in a heavy tailed waiting time distribution characterized by α=1. We provide empirical evidence supporting the relevance of these two models to human activity patterns, showing that while emails, web browsing and library visitation display α=1, the surface mail based communication belongs to the α=3∕2 universality class. Finally, we discuss possible extension of the proposed queuing models and outline some future challenges in exploring the statistical mechanics of human dynamics.Mon Aug 22 22:58:12 CEST 2011Berlin/HeidelbergThe Semantic Web: Research and Applications10.1007/11762256_38514--529Lecture Notes in Computer ScienceSemantic Network Analysis of Ontologies401120062006 iccs_example l3s myown ontology semantic trias_example sna analysis network social A key argument for modeling knowledge in ontologies is the easy reuse 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. Fri May 13 11:03:18 CEST 2011MünchenInformation Technologymay3101--107Enhancing Social Interactions at Conferences5320112011 computing conferator conference myown network rfid social ubiquitous Conferator is a novel social conference system that provides the management of social interactions and context information in ubiquitous and social environments. Using RFID and social networking technology, Conferator provides the means for effective management of personal contacts and according conference information before, during and after a conference. We describe the system in detail, before we analyze and discuss results of a typical application of the Conferator system.Thu Jan 27 13:37:04 CET 2011Amsterdam, The NetherlandsAI Communicationsdec4245--262Network Properties of Folksonomies202007folksonomy ol_tut2010 property sna analysis network social Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures - so-called folksonomies - as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam. Thu Jan 27 12:08:28 CET 2011New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata20082008 engine information l3s logsonomy myown retrieval search wp5 analysis network sna social Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.Wed Oct 13 11:12:49 CEST 2010E & I Elektrotechnik und Informationstechnik10341--346Wireless sensor network approach for robust localization of mobile nodes with minimal complexity1252008localization network sensor venus The actual paper introduces a concept for localization of mobile nodes in a wireless sensor network. The realized algorithms are characterized by minimal complexity and high robustness even in networks with scarce resources. The implementation on simple, low-power embedded systems is possible without difficulty. An application of the concept for vehicle tracking illustrates the very good performance of the approach.Thu Aug 12 15:01:57 CEST 2010New York, NY, USAHT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia265--270Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy2010bibsonomy collaborative community detection evidence folksonomy network tagging The ongoing spread of online social networking and sharing sites has reshaped the way how people interact with each other. Analyzing the relatedness of different users within the resulting large populations of these systems plays an important role for tasks like user recommendation or community detection. Algorithms in these fields typically face the problem that explicit user relationships (like friend lists) are often very sparse. Surprisingly, implicit evidences (like click logs) of user relations have hardly been considered to this end. Based on our long-time experience with running BibSonomy [4], we identify in this paper different evidence networks of user relationships in our system. We broadly classify each network based on whether the links are explicitly established by the users (e.g., friendship or group membership) or accrue implicitly in the running system (e.g., when user u copies an entry of user v). We systematically analyze structural properties of these networks and whether topological closeness (in terms of the length of shortest paths) coincides with semantic similarity between users.Mon Jul 12 11:33:29 CEST 2010New York, NY, USACIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management1545--1548Exploit the tripartite network of social tagging for web clustering2009clustering mode network tagging three triadic In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.Wed Apr 28 14:11:10 CEST 2010HiPCconf/hipc/2007197-208Lecture Notes in Computer ScienceAccelerating Large Graph Algorithms on the GPU Using CUDA.48732007analysis cuda gpu graph network programming Thu Jan 28 22:17:21 CET 2010Proceedings on the W3C Workshop on the Future of Social NetworkingIntegrating Social Networks and Sensor Networks2009dagsocial network sensor social venus Sensors have begun to infiltrate people's everyday lives. They can provide information about a car's condition, can enable smart buildings, and are being used in various mobile applications, to name a few. Generally, sensors provide information about various aspects of the real world. Online social networks, another emerging trend over the past six or seven years, can provide insights into the communication links and patterns between people. They have enabled novel developments in communications as well as transforming the Web from a technical infrastructure to a social platform, very much along the lines of the original Web as proposed by Tim Berners-Lee, which is now often referred to as the Social Web. In this position paper, we highlight some of the interesting research areas where sensors and social networks can fruitfully interface, from sensors providing contextual information in context-aware and personalized social applications, to using social networks as "storage infrastructures" for sensor information.Tue Jan 26 14:50:28 CET 2010Informatik Spektrum2163--167Aktuelles Schlagwort: Complex Event Processing (CEP)322009cep complex event network processing sensor venus Ereignisgesteuerte Informationssysteme benötigen eine systematische und automatische Verarbeitung von Ereignissen. Complex Event Processing (CEP) ist ein Sammelbegriff für Methoden, Techniken und Werkzeuge, um Ereignisse zu verarbeiten während sie passieren, also kontinuierlich und zeitnah. CEP leitet aus Ereignissen höheres, wertvolles Wissen in Form von sog. komplexen Ereignissen, d.h. Situationen die sich nur als Kombination mehrerer Ereignisse erkennen lassen, ab. Tue Jan 26 14:49:25 CET 2010PMS-FB-2009-6Complex Event Processing (CEP){research report, PMS-FB-2009-6}2009cep complex event network processing sensor venus Event-driven information systems demand a systematic and automatic processing of events. Complex Event Processing (CEP) encompasses methods, techniques, and tools for processing events while they occur, i.e., in a continuous and timely fashion. CEP derives valuable higher-level knowledge from lower-level events; this knowledge takes the form of so called complex events, that is, situations that can only be recognized as a combination of several events.Wed Aug 05 12:39:24 CEST 2009New York, NY, USA27Structural Analysis in the Social SciencesExploratory Social Network Analysis with Pajek2005graph pajek tool visualisation sna analysis network social Wed Jun 10 10:17:59 CEST 2009New York, NY, USAProceedings of the 16th International Conference on World Wide Web835--844Analysis of topological characteristics of huge online social networking services2007folksonomy online analysis network sna social Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Very interestingly, each of the two e ponents corresponds to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks.Wed Jun 10 10:14:02 CEST 2009Physical Review Lettersoct20208701Assortative Mixing in Networks892002assortative author graph mixing newman sna analysis network social A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.