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020 _a9783642149290
_9978-3-642-14929-0
040 _cMX-MeUAM
050 4 _aQA76.76.A65
082 0 4 _a005.7
_223
100 1 _aGiles, Lee.
_eeditor.
245 1 0 _aAdvances in Social Network Mining and Analysis
_h[recurso electrónico] :
_bSecond International Workshop, SNAKDD 2008, Las Vegas, NV, USA, August 24-27, 2008 /
_cedited by Lee Giles, Marc Smith, John Yen, Haizheng Zhang.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aXI, 131p. 47 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v5498
505 0 _aLeveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study -- Community Detection Using a Measure of Global Influence -- Communication Dynamics of Blog Networks -- Finding Spread Blockers in Dynamic Networks -- Social Network Mining with Nonparametric Relational Models -- Using Friendship Ties and Family Circles for Link Prediction -- Information Theoretic Criteria for Community Detection.
520 _aThis year’s volume of Advances in Social Network Analysis contains the p- ceedings for the Second International Workshop on Social Network Analysis (SNAKDD 2008). The annual workshop co-locates with the ACM SIGKDD - ternational Conference on Knowledge Discovery and Data Mining (KDD). The second SNAKDD workshop was held with KDD 2008 and received more than 32 submissions on social network mining and analysis topics. We accepted 11 regular papers and 8 short papers. Seven of the papers are included in this volume. In recent years, social network research has advanced signi?cantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale o?ine social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are - creasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topol- ical properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining socialnetworks.These issues haveimportant implications oncom- nitydiscovery,anomalydetection,trendpredictionandcanenhanceapplications in multiple domains such as information retrieval, recommendation systems, - curity and so on.
650 0 _aComputer science.
650 0 _aComputer Communication Networks.
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aInformation systems.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aInformation Systems Applications (incl.Internet).
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aDatabase Management.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aComputer Communication Networks.
700 1 _aSmith, Marc.
_eeditor.
700 1 _aYen, John.
_eeditor.
700 1 _aZhang, Haizheng.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642149283
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v5498
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-14929-0
596 _a19
942 _cLIBRO_ELEC
999 _c202777
_d202777