BibSonomy publications for /user/hotho/networkWed May 21 10:23:17 CEST 2008AI Communications4245 - 262Network Properties of Folksonomies202007network properties kdubiq 2007 sosbuch myown folksonomies summerschool Sat Apr 26 13:11:10 CEST 2008arXivJanSocial Information Processing in Social News Aggregation2007social digg flickr toread dynamics network The rise of the social media sites, such as blogs, wikis, Digg and Flickr among others, underscores the transformation of the Web to a participatory medium in which users are collaboratively creating, evaluating and distributing information. The innovations introduced by social media has lead to a new paradigm for interacting with information, what we call 'social information processing'. In this paper, we study how social news aggregator Digg exploits social information processing to solve the problems of document recommendation and rating. First, we show, by tracking stories over time, that social networks play an important role in document recommendation. The second contribution of this paper consists of two mathematical models. The first model describes how collaborative rating and promotion of stories emerges from the independent decisions made by many users. The second model describes how a user's influence, the number of promoted stories and the user's social network, changes in time. We find qualitative agreement between predictions of the model and user data gathered from Digg.Sat Apr 05 17:54:56 CEST 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy2008icwsm folksonomy logsonomy search log analysis query 2008 network myown In social bookmarking 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. Search engines filter the vast information of the web. Queries 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. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.Thu Jan 24 17:46:20 CET 2008Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007)MarchNominated for Best Paper Award{On the Structure, Properties and Utility of Internal Corporate Blogs}2007network kdubiq summerschool analysis properties blog Thu Jan 24 17:17:49 CET 2008CoRRinformal publicationPreferential attachment in the growth of social networks: the case of Wikipediaabs/physics/06020262006social network summerschool wikipedia kdubiq sna Tue Jan 15 10:57:12 CET 2008LjubljanaData Science and Classification (Proc. IFCS 2006 Conference)July261-270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies2006association 2006 rules analysis folksonomy summerschool network seminar2006 myown kdubiq sosbuch semantic Tue Jan 15 10:29:05 CET 2008Budva, MontenegroProceedings of the 3rd European Semantic Web ConferenceJune514-529LNCSSemantic Network Analysis of Ontologies40112006ontology 2006 myown web sna network social analysis semantic Fri Nov 02 15:40:58 CET 2007BanffProc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization''MayNetwork Properties of Folksonomies20072007 sna workshop folksonomy myown network 8Fri Oct 05 15:18:51 CEST 2007AugThe Structure of Collaborative Tagging Systems2005summerschool folksonomy tagging network kdubiq sosbuch social Fri Jul 06 11:51:49 CEST 2007New York, NY, USASIGIR '88: Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval147--160On the use of spreading activation methods in automatic information1988msn evaluation ir semantic *** spreading network activation Spreading activation methods have been recommended in information retrieval to expand the search vocabulary and to complement the retrieved document sets. The spreading activation strategy is reminiscent of earlier associative indexing and retrieval systems. Some spreading activation procedures are briefly described, and evaluation output is given, reflecting the effectiveness of one of the proposed procedures.Wed Jul 04 16:49:33 CEST 2007Artificial Intelligence ReviewDecember6453--482Application of Spreading Activation Techniques in Information Retrieval111997spreading search network activation *** retrieval ir survey information msn semantic This paper surveys the use of Spreading Activation techniques onSemantic Networks in Associative Information Retrieval. The majorSpreading Activation models are presented and their applications toIR is surveyed. A number of works in this area are criticallyanalyzed in order to study the relevance of Spreading Activation forassociative IR. ER -Tue Jun 19 11:11:27 CEST 2007Annual Review of SociologyThis article consists of 30 page(s)415-444Birds of a Feather: Homophily in Social Networks 272001speed toread network Similarity breeds connection. This principle-the homophily principle-structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localized positions) within social space. We argue for more research on: (a) the basic ecological processes that link organizations, associations, cultural communities, social movements, and many other social forms; (b) the impact of multiplex ties on the patterns of homophily; and (c) the dynamics of network change over time through which networks and other social entities co-evolve.Tue Jun 19 10:57:58 CEST 2007Journal of Computational & Graphical Statistics584-608(25)Building an Effective Representation for Dynamic Networks15September 2006data network representation toread A dynamic network is a special type of network composed of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to efficient large-scale analysis is a challenge. In this article, we represent dynamic graphs using a data structure introduced in an earlier article. We advocate their representation because it accounts for the evolution of relationships between transactors through time, mitigates noise at the local transactor level, and allows for the removal of stale relationships. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task. In addition, we present a preliminary analysis on Web logs and e-mail networks. Mon Jun 04 16:40:42 CEST 2007Proceedings of the European Semantic Web Conference, ESWC2007JulyLecture Notes in Computer Science{Extracting Social Networks among Various Entities on the Web}45192007relation network toread social extraction Wed May 23 18:07:35 CEST 2007Tracking User Attention in Collaborative Tagging Communities2007kdubiq clustering analysis summerschool ***** bibsonomy property tagging network Tue Mar 13 17:57:29 CET 2007CEASconf/ceas/2005Scalable and Reliable Collaborative Spam Filters: Harnessing the Global Social Email Networks.2005email network spam social Tue Mar 13 17:56:43 CET 2007Reputation Network Analysis for Email Filtering2004email network spam Tue Mar 13 17:55:17 CET 2007Comparative Graph Theoretical Characterization of Networks of Spam and Legitimate Email2005email network graph spam Email is an increasingly important and ubiquitous means of communication, both facilitating contact between private individuals and enabling rises in the productivity of organizations. However the relentless rise of automatic unauthorized emails, a.k.a. spam is eroding away much of the attractiveness of email communication. Most of the attention dedicated to date to spam detection has focused on the content of the emails or on the addresses or domains associated with spam senders. Although methods based on these - easily changeable - identifiers work reasonably well they miss on the fundamental nature of spam as an opportunistic relationship, very different from the normal mutual relations between senders and recipients of legitimate email. Here we present a comprehensive graph theoretical analysis of email traffic that captures these properties quantitatively. We identify several simple metrics that serve both to distinguish between spam and legitimate email and to provide a statistical basis for models of spam traffic.Fri Mar 09 11:36:28 CET 2007Proceedings of the EACL 2006 Workshop on New Text-Wikis and blogs and other dynamic text sources1-8Text Linkage in the Wiki Medium-A comparative study2006network wikipedia toread social Fri Feb 23 14:19:24 CET 2007KDDconf/kdd/200644-54Group formation in large social networks: membership, growth, and evolution.2006community evolution toread social network