Professor Barabási's talk described how the tools of network science can help understand the Web's structure, development and weaknesses. The Web is an information network, in which the nodes are documents (at the time of writing over one trillion of them), connected by links. Other well-known network structures include the Internet, a physical network where the nodes are routers and the links are physical connections, and organizations, where the nodes are people and the links represent communications.

@article{barabsi2013network, author = {Barabási, Albert-László}, title = {Network science}, journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, year = {2013}, volume = {371}, number = {1987}, url = {http://rsta.royalsocietypublishing.org/content/371/1987/20120375.abstract}, doi = {10.1098/rsta.2012.0375}, keywords = {analysis, network, sna, social, webscience}, abstract = {Professor Barabási's talk described how the tools of network science can help understand the Web's structure, development and weaknesses. The Web is an information network, in which the nodes are documents (at the time of writing over one trillion of them), connected by links. Other well-known network structures include the Internet, a physical network where the nodes are routers and the links are physical connections, and organizations, where the nodes are people and the links represent communications.} }

%0 = article %A = Barabási, Albert-László %D = 2013 %T = Network science %U = http://rsta.royalsocietypublishing.org/content/371/1987/20120375.abstract

The growth of the Web has required us to think about the design of information systems in which large-scale computational and social feedback effects are simultaneously at work. At the same time, the data generated by Web-scale systems—recording the ways in which millions of participants create content, link information, form groups and communicate with one another—have made it possible to evaluate long-standing theories of social interaction, and to formulate new theories based on what we observe. These developments have created a new level of interaction between computing and the social sciences, enriching the perspectives of both of these disciplines. We discuss some of the observations, theories and conclusions that have grown from the study of Web-scale social interaction, focusing on issues including the mechanisms by which people join groups, the ways in which different groups are linked together in social networks and the interplay of positive and negative interactions in these networks.

@article{kleinberg2013analysis, author = {Kleinberg, Jon}, title = {Analysis of large-scale social and information networks}, journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, year = {2013}, volume = {371}, number = {1987}, url = {http://rsta.royalsocietypublishing.org/content/371/1987/20120378.abstract}, doi = {10.1098/rsta.2012.0378}, keywords = {analysis, information, network, sna, social, webscience}, abstract = {The growth of the Web has required us to think about the design of information systems in which large-scale computational and social feedback effects are simultaneously at work. At the same time, the data generated by Web-scale systems—recording the ways in which millions of participants create content, link information, form groups and communicate with one another—have made it possible to evaluate long-standing theories of social interaction, and to formulate new theories based on what we observe. These developments have created a new level of interaction between computing and the social sciences, enriching the perspectives of both of these disciplines. We discuss some of the observations, theories and conclusions that have grown from the study of Web-scale social interaction, focusing on issues including the mechanisms by which people join groups, the ways in which different groups are linked together in social networks and the interplay of positive and negative interactions in these networks.} }

%0 = article %A = Kleinberg, Jon %D = 2013 %T = Analysis of large-scale social and information networks %U = http://rsta.royalsocietypublishing.org/content/371/1987/20120378.abstract

Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific socially informed model for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters.

@article{birkholz2012scalable, author = {Birkholz, Julie M. and Bakhshi, Rena and Harige, Ravindra and van Steen, Maarten and Groenewegen, Peter}, title = {Scalable Analysis of Socially Informed Network Models: the data-aware mean-field approach }, journal = {CoRR}, year = {2012}, volume = {abs/1209.6615}, url = {http://arxiv.org/abs/1209.6615}, keywords = {analysis, network, scalability, scientometrics, social, toread}, abstract = {Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific socially informed model for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters. } }

%0 = article %A = Birkholz, Julie M. and Bakhshi, Rena and Harige, Ravindra and van Steen, Maarten and Groenewegen, Peter %D = 2012 %T = Scalable Analysis of Socially Informed Network Models: the data-aware mean-field approach %U = http://arxiv.org/abs/1209.6615

We introduce the concepts of closed sets and closure operators as mathematical tools for the study of social networks. Dynamic networks are represented by transformations. It is shown that under continuous change/transformation, all networks tend to "break down" and become less complex. It is a kind of entropy. The product of this theoretical decomposition is an abundance of triadically closed clusters which sociologists have observed in practice. This gives credence to the relevance of this kind of mathematical analysis in the sociological context.

@inproceedings{pfaltz2012entropy, author = {Pfaltz, John L.}, title = {Entropy in Social Networks}, booktitle = {Proceedings of the SOCINFO}, year = {2012}, keywords = {analysis, closure, entropy, network, sna, social, toread}, abstract = {We introduce the concepts of closed sets and closure operators as mathematical tools for the study of social networks. Dynamic networks are represented by transformations. It is shown that under continuous change/transformation, all networks tend to "break down" and become less complex. It is a kind of entropy. The product of this theoretical decomposition is an abundance of triadically closed clusters which sociologists have observed in practice. This gives credence to the relevance of this kind of mathematical analysis in the sociological context. } }

%0 = inproceedings %A = Pfaltz, John L. %B = Proceedings of the SOCINFO %D = 2012 %T = Entropy in Social Networks

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.

@article{atzmueller2011enhancing, author = {Atzmueller, Martin and Benz, Dominik and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Macek, Bjoern Elmar and Mitzlaff, Folke and Scholz, Christoph and Stumme, Gerd}, title = {Enhancing Social Interactions at Conferences}, journal = {Information Technology}, publisher = {Oldenbourg Wissenschaftsverlag}, address = {München}, year = {2011}, volume = {53}, number = {3}, pages = {101--107}, url = {http://www.oldenbourg-link.com/doi/abs/10.1524/itit.2011.0631}, doi = {10.1524/itit.2011.0631}, issn = {1611-2776}, keywords = {2011, computing, conferator, conference, myown, network, rfid, social, ubiquitous}, abstract = {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.} }

%0 = article %A = Atzmueller, Martin and Benz, Dominik and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Macek, Bjoern Elmar and Mitzlaff, Folke and Scholz, Christoph and Stumme, Gerd %C = München %D = 2011 %I = Oldenbourg Wissenschaftsverlag %T = Enhancing Social Interactions at Conferences %U = http://www.oldenbourg-link.com/doi/abs/10.1524/itit.2011.0631

The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on “

@article{batagelj2011algorithms, author = {Batagelj, Vladimir and Zaveršnik, Matjaž}, title = {Fast algorithms for determining (generalized) core groups in social networks}, journal = {Advances in Data Analysis and Classification}, publisher = {Springer}, year = {2011}, volume = {5}, number = {2}, pages = {129--145}, url = {http://dx.doi.org/10.1007/s11634-010-0079-y}, doi = {10.1007/s11634-010-0079-y}, issn = {1862-5347}, keywords = {analysis, core, network, sna, social}, abstract = {The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on “} }

%0 = article %A = Batagelj, Vladimir and Zaveršnik, Matjaž %D = 2011 %I = Springer %T = Fast algorithms for determining (generalized) core groups in social networks %U = http://dx.doi.org/10.1007/s11634-010-0079-y

In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.

@article{pham2011development, author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias}, title = {Development of computer science disciplines: a social network analysis approach}, journal = {Social Network Analysis and Mining}, publisher = {Springer}, address = {Wien}, year = {2011}, volume = {1}, number = {4}, pages = {321--340}, url = {http://dx.doi.org/10.1007/s13278-011-0024-x}, doi = {10.1007/s13278-011-0024-x}, issn = {1869-5450}, keywords = {analysis, computer, network, science, scientometrics, sna, social}, abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.} }

%0 = article %A = Pham, Manh and Klamma, Ralf and Jarke, Matthias %C = Wien %D = 2011 %I = Springer %T = Development of computer science disciplines: a social network analysis approach %U = http://dx.doi.org/10.1007/s13278-011-0024-x

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.

@inproceedings{weller2011citation, author = {Weller, Katrin and Dröge, Evelyn and Puschmann, Cornelius}, title = {Citation Analysis in Twitter: Approaches for Defining and Measuring Information Flows within Tweets during Scientific Conferences}, editor = {Rowe, Matthew and Stankovic, Milan and Dadzie, Aba-Sah and Hardey, Mariann}, booktitle = {Making Sense of Microposts (MSM2011)}, year = {2011}, pages = {1--12}, url = {http://ceur-ws.org/Vol-718/paper_04.pdf}, keywords = {analysis, citation, microblogging, network, twitter}, abstract = {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.} }

%0 = inproceedings %A = Weller, Katrin and Dröge, Evelyn and Puschmann, Cornelius %B = Making Sense of Microposts (MSM2011) %D = 2011 %T = Citation Analysis in Twitter: Approaches for Defining and Measuring Information Flows within Tweets during Scientific Conferences %U = http://ceur-ws.org/Vol-718/paper_04.pdf

The VIVO project is creating an open, Semantic Web-based network of institutional ontology-driven databases to enable national discovery, networking, and collaboration via information sharing about researchers and their activities. The project has been funded by NIH to implement VIVO at the University of Florida, Cornell University, and Indiana University Bloomington together with four other partner institutions. Working with the Semantic Web/Linked Open Data community, the project will pilot the development of common ontologies, integration with institutional information sources and authentication, and national discovery and exploration of networks of researchers. Building on technology developed over the last five years at Cornell University, VIVO supports the flexible description and interrelation of people, organizations, activities, projects, publications, affiliations, and other entities and properties. VIVO itself is an open source Java application built on W3C Semantic Web standards, including RDF, OWL, and SPARQL. To create researcher profiles, VIVO draws on authoritative information from institutional databases, external data sources such as PubMed, and information provided directly by researchers themselves. While the NIH-funded project focuses on biomedical research, the current Cornell implementation of VIVO supports the full range of disciplines across the university, from music to mechanical engineering to management. There are many ways a person?s expertise may be discovered, through grants, presentations, courses and news releases, as well as through research statements or publications listed on their profile-resulting in the creation of implicit groups or networks of people based on a number of pre-identified, shared characteristics. In addition to formal authoritative information and relationships, VIVO can also support the creation of personal work groups and associated properties to represent the informal relationships evolving around collaboration.

@inproceedings{krafft2010enabling, author = {Krafft, Dean B. and Cappadona, Nicholas A. and Caruso, Brian and Corson-Rikert, Jon and Devare, Medha and Lowe, Brian J. and Collaboration, VIVO}, title = {VIVO: Enabling National Networking of Scientists}, booktitle = {WebSci10: Extending the Frontiers of Society On-Line}, year = {2010}, url = {http://journal.webscience.org/316/}, keywords = {gaw, network, research, science, university, vivo}, abstract = {The VIVO project is creating an open, Semantic Web-based network of institutional ontology-driven databases to enable national discovery, networking, and collaboration via information sharing about researchers and their activities. The project has been funded by NIH to implement VIVO at the University of Florida, Cornell University, and Indiana University Bloomington together with four other partner institutions. Working with the Semantic Web/Linked Open Data community, the project will pilot the development of common ontologies, integration with institutional information sources and authentication, and national discovery and exploration of networks of researchers. Building on technology developed over the last five years at Cornell University, VIVO supports the flexible description and interrelation of people, organizations, activities, projects, publications, affiliations, and other entities and properties. VIVO itself is an open source Java application built on W3C Semantic Web standards, including RDF, OWL, and SPARQL. To create researcher profiles, VIVO draws on authoritative information from institutional databases, external data sources such as PubMed, and information provided directly by researchers themselves. While the NIH-funded project focuses on biomedical research, the current Cornell implementation of VIVO supports the full range of disciplines across the university, from music to mechanical engineering to management. There are many ways a person?s expertise may be discovered, through grants, presentations, courses and news releases, as well as through research statements or publications listed on their profile--resulting in the creation of implicit groups or networks of people based on a number of pre-identified, shared characteristics. In addition to formal authoritative information and relationships, VIVO can also support the creation of personal work groups and associated properties to represent the informal relationships evolving around collaboration.} }

%0 = inproceedings %A = Krafft, Dean B. and Cappadona, Nicholas A. and Caruso, Brian and Corson-Rikert, Jon and Devare, Medha and Lowe, Brian J. and Collaboration, VIVO %B = WebSci10: Extending the Frontiers of Society On-Line %D = 2010 %T = VIVO: Enabling National Networking of Scientists %U = http://journal.webscience.org/316/

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

@article{gansner2009drawing, author = {Gansner, Emden R. and Hu, Yifan and Kobourov, Stephen G.}, title = {GMap: Drawing Graphs as Maps}, journal = {cs.CG}, year = {2009}, volume = {arXiv:0907.2585v1}, url = {http://arxiv.org/abs/0907.2585}, keywords = {analysis, citation, drawing, gmap, graph, graphics, graphviz, network, sna, social, visualization}, abstract = {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 } }

%0 = article %A = Gansner, Emden R. and Hu, Yifan and Kobourov, Stephen G. %D = 2009 %T = GMap: Drawing Graphs as Maps %U = http://arxiv.org/abs/0907.2585

Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make delicious a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly "similar" users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. We investigate the question whether friends have common interests, we gain additional insights on the strategies that users use to assign tags to their bookmarks, and we demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution.

@inproceedings{angelova2008characterizing, author = {Angelova, Ralitsa and Lipczak, Marek and Milios, Evangelos and Prałat, Paweł}, title = {Characterizing a social bookmarking and tagging network}, booktitle = {Proceedings of the Mining Social Data Workshop (MSoDa)}, year = {2008}, pages = {21--25}, url = {http://www.math.ryerson.ca/~pralat/papers/2008_delicious.pdf}, keywords = {analysis, bookmarking, collaborative, folksonomy, network, tagging}, abstract = {Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make delicious a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly "similar" users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. We investigate the question whether friends have common interests, we gain additional insights on the strategies that users use to assign tags to their bookmarks, and we demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution. } }

%0 = inproceedings %A = Angelova, Ralitsa and Lipczak, Marek and Milios, Evangelos and Prałat, Paweł %B = Proceedings of the Mining Social Data Workshop (MSoDa) %D = 2008 %T = Characterizing a social bookmarking and tagging network %U = http://www.math.ryerson.ca/~pralat/papers/2008_delicious.pdf

In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms.

@inproceedings{zesch2007analysis, author = {Zesch, Torsten and Gurevych, Iryna}, title = {Analysis of the Wikipedia Category Graph for NLP Applications}, booktitle = {Proceedings of the TextGraphs-2 Workshop (NAACL-HLT)}, publisher = {Association for Computational Linguistics}, address = {Rochester}, year = {2007}, pages = {1--8}, url = {http://acl.ldc.upenn.edu/W/W07/W07-02.pdf#page=11}, keywords = {analysis, category, graph, language, natural, network, nlp, processing, wikipedia}, abstract = {In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms. } }

%0 = inproceedings %A = Zesch, Torsten and Gurevych, Iryna %B = Proceedings of the TextGraphs-2 Workshop (NAACL-HLT) %C = Rochester %D = 2007 %I = Association for Computational Linguistics %T = Analysis of the Wikipedia Category Graph for NLP Applications %U = http://acl.ldc.upenn.edu/W/W07/W07-02.pdf#page=11

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.

@incollection{hoser2006semantic, author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {Semantic Network Analysis of Ontologies}, editor = {Sure, York and Domingue, John}, booktitle = {The Semantic Web: Research and Applications}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2006}, volume = {4011}, pages = {514--529}, note = {10.1007/11762256_38}, url = {http://dx.doi.org/10.1007/11762256_38}, doi = {10.1007/11762256_38}, isbn = {978-3-540-34544-2}, keywords = {2006, iccs_example, l3s, myown, ontology, semantic, trias_example, sna, analysis, network, social}, abstract = { 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. } }

%0 = incollection %A = Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd %B = The Semantic Web: Research and Applications %C = Berlin/Heidelberg %D = 2006 %I = Springer %T = Semantic Network Analysis of Ontologies %U = http://dx.doi.org/10.1007/11762256_38

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.

@article{newman2006modularity, author = {Newman, M. E. J.}, title = {Modularity and community structure in networks}, journal = {Proceedings of the National Academy of Sciences}, year = {2006}, volume = {103}, number = {23}, pages = {8577--8582}, doi = {10.1073/pnas.0601602103}, keywords = {clustering, community, graph, modularity, network, structure}, abstract = {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.} }

%0 = article %A = Newman, M. E. J. %D = 2006 %T = Modularity and community structure in networks

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.

@article{vazquez2006modeling, author = {Vázquez, Alexei and Gama Oliveira, João and Dezsö, Zoltán and Goh, Kwang-Il and Kondor, Imre and Barabási, Albert-László}, title = {Modeling bursts and heavy tails in human dynamics}, journal = {Physical Review E}, publisher = {American Physical Society}, year = {2006}, volume = {73}, number = {3}, pages = {036127}, url = {http://link.aps.org/doi/10.1103/PhysRevE.73.036127}, doi = {10.1103/PhysRevE.73.036127}, keywords = {dynamics, sna, analysis, network, social}, abstract = { 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.} }

%0 = article %A = Vázquez, Alexei and Gama Oliveira, João and Dezsö, Zoltán and Goh, Kwang-Il and Kondor, Imre and Barabási, Albert-László %D = 2006 %I = American Physical Society %T = Modeling bursts and heavy tails in human dynamics %U = http://link.aps.org/doi/10.1103/PhysRevE.73.036127

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.

@article{barrat2004architecture, author = {Barrat, A. and Barthélemy, M. and Pastor-Satorras, R. and Vespignani, A.}, title = {The architecture of complex weighted networks}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, year = {2004}, volume = {101}, number = {11}, pages = {3747--3752}, url = {http://www.pnas.org/content/101/11/3747.abstract}, doi = {10.1073/pnas.0400087101}, keywords = {complex, sna, analysis, network, social}, abstract = {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.} }

%0 = article %A = Barrat, A. and Barthélemy, M. and Pastor-Satorras, R. and Vespignani, A. %D = 2004 %T = The architecture of complex weighted networks %U = http://www.pnas.org/content/101/11/3747.abstract

A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems.Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.

@inproceedings{yu2003searching, author = {Yu, Bin and Singh, Munindar P.}, title = {Searching social networks}, booktitle = {Proceedings of the second international joint conference on Autonomous agents and multiagent systems}, publisher = {ACM}, address = {New York, NY, USA}, year = {2003}, pages = {65--72}, url = {http://doi.acm.org/10.1145/860575.860587}, doi = {10.1145/860575.860587}, isbn = {1-58113-683-8}, keywords = {agent, collaborative, network, search, social, web}, abstract = {A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems.Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.} }

%0 = inproceedings %A = Yu, Bin and Singh, Munindar P. %B = Proceedings of the second international joint conference on Autonomous agents and multiagent systems %C = New York, NY, USA %D = 2003 %I = ACM %T = Searching social networks %U = http://doi.acm.org/10.1145/860575.860587

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.

@article{newman2001structure, author = {Newman, M. E. J.}, title = {The structure of scientific collaboration networks}, journal = {Proceedings of the National Academy of Sciences}, year = {2001}, volume = {98}, number = {2}, pages = {404--409}, url = {http://www.pnas.org/content/98/2/404.abstract}, doi = {10.1073/pnas.98.2.404}, keywords = {analysis, citation, network, science, scientometrics}, abstract = {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.} }

%0 = article %A = Newman, M. E. J. %D = 2001 %T = The structure of scientific collaboration networks %U = http://www.pnas.org/content/98/2/404.abstract

Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.

@incollection{yu2000social, author = {Yu, Bin and Singh, Munindar}, title = {A Social Mechanism of Reputation Management in Electronic Communities}, editor = {Klusch, Matthias and Kerschberg, Larry}, booktitle = {Cooperative Information Agents IV - The Future of Information Agents in Cyberspace}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2000}, volume = {1860}, pages = {355--393}, url = {http://dx.doi.org/10.1007/978-3-540-45012-2_15}, doi = {10.1007/978-3-540-45012-2_15}, isbn = {978-3-540-67703-1}, keywords = {collaborative, community, management, network, quality, reputation, search, social, trust, web}, abstract = {Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.} }

%0 = incollection %A = Yu, Bin and Singh, Munindar %B = Cooperative Information Agents IV - The Future of Information Agents in Cyberspace %C = Berlin/Heidelberg %D = 2000 %I = Springer %T = A Social Mechanism of Reputation Management in Electronic Communities %U = http://dx.doi.org/10.1007/978-3-540-45012-2_15

@article{kautz1997referral, author = {Kautz, Henry and Selman, Bart and Shah, Mehul}, title = {Referral Web: combining social networks and collaborative filtering}, journal = {Communications of the ACM}, publisher = {ACM}, address = {New York, NY, USA}, year = {1997}, volume = {40}, number = {3}, pages = {63--65}, url = {http://doi.acm.org/10.1145/245108.245123}, doi = {10.1145/245108.245123}, issn = {0001-0782}, keywords = {collaborative, filtering, hybrid, network, recommender, social, stair} }

%0 = article %A = Kautz, Henry and Selman, Bart and Shah, Mehul %C = New York, NY, USA %D = 1997 %I = ACM %T = Referral Web: combining social networks and collaborative filtering %U = http://doi.acm.org/10.1145/245108.245123