Brian Gallagher, Tina Eliassi-Rad (auth.), Lee Giles, Marc's Advances in Social Network Mining and Analysis: Second PDF

By Brian Gallagher, Tina Eliassi-Rad (auth.), Lee Giles, Marc Smith, John Yen, Haizheng Zhang (eds.)

ISBN-10: 3642149286

ISBN-13: 9783642149283

This year’s quantity of Advances in Social community research comprises the p- ceedings for the second one foreign Workshop on Social community research (SNAKDD 2008). the once a year workshop co-locates with the ACM SIGKDD - ternational convention on wisdom Discovery and knowledge Mining (KDD). the second one SNAKDD workshop was once held with KDD 2008 and acquired greater than 32 submissions on social community mining and research themes. We approved eleven general papers and eight brief papers. Seven of the papers are integrated during this quantity. in recent times, social community study has complex signi?cantly, because of the superiority of the web social web pages and immediate messaging platforms in addition to the supply of quite a few large-scale o?ine social community structures. those social community structures are typically characterised by way of the advanced community buildings and wealthy accompanying contextual info. Researchers are - creasingly drawn to addressing a variety of demanding situations living in those disparate social community platforms, together with making a choice on universal static topol- ical houses and dynamic homes throughout the formation and evolution of those social networks, and the way contextual info will help in interpreting the pertaining socialnetworks.These concerns haveimportant implications oncom- nitydiscovery,anomalydetection,trendpredictionandcanenhanceapplications in a number of domain names equivalent to info retrieval, suggestion platforms, - curity and so on.

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Extra resources for Advances in Social Network Mining and Analysis: Second International Workshop, SNAKDD 2008, Las Vegas, NV, USA, August 24-27, 2008

Example text

In other words, users who uploaded one of the 500 most interesting images retrieved by the query tiger, were labeled wildlife, whether or not they were interested in wildlife photography. The contacts and contacts’s contacts of the four users within this set identified as being interested in wildlife photography were also labeled wildlife. Although we did not verify that all the labeled users were indeed interested in the topic, we use these soft labels to evaluate the discovered communities. Once we retrieved the social networks of target set of users, we reduced it to an undirected network containing mutual contacts only.

Classification in networked data: A toolkit and a univariate case study. Journal of Machine Learning Research 8, 935–983 (2007) 8. : Learning relational probability trees. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 625–630 (2003) 9. : Aggregation-based feature invention and relational concept classes. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 167–176 (2003) 10. : Pruning social networks using structural properties and descriptive attributes.

Edu/∼mejn/netdata/ 30 R. Ghosh and K. , wildlife for tiger and beetle query terms, portraiture for the newborn query, and technology for the apple query. We studied each user’s profile to confirm that the user was indeed interested in that topic. Specifically, we looked at group membership and user’s most common tags. Thus, groups such as “Big Cats”, “Zoo”, “The Wildlife Photography”, etc. pointed to user’s interest in the wildlife topic. In addition to group membership, tags that users attached to their images could also help identify their interests.

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Advances in Social Network Mining and Analysis: Second International Workshop, SNAKDD 2008, Las Vegas, NV, USA, August 24-27, 2008 by Brian Gallagher, Tina Eliassi-Rad (auth.), Lee Giles, Marc Smith, John Yen, Haizheng Zhang (eds.)


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