Titlebook: Advances in Social Network Mining and Analysis; Second International Lee Giles,Marc Smith,Haizheng Zhang Conference proceedings 2010 The Ed

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书目名称Advances in Social Network Mining and Analysis影响因子(影响力)




书目名称Advances in Social Network Mining and Analysis影响因子(影响力)学科排名




书目名称Advances in Social Network Mining and Analysis网络公开度




书目名称Advances in Social Network Mining and Analysis网络公开度学科排名




书目名称Advances in Social Network Mining and Analysis被引频次




书目名称Advances in Social Network Mining and Analysis被引频次学科排名




书目名称Advances in Social Network Mining and Analysis年度引用




书目名称Advances in Social Network Mining and Analysis年度引用学科排名




书目名称Advances in Social Network Mining and Analysis读者反馈




书目名称Advances in Social Network Mining and Analysis读者反馈学科排名

CBC471

Christopher Frauenberger,Winfried Ritschld produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics. We describe model

和平主义者

Dungeon

逃避系列单词

0302-9743 allenges 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 c978-3-642-14928-3978-3-642-14929-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

Small-Intestine

不妥协

Communication Dynamics of Blog Networks,ld produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics. We describe model

不可救药

Finding Spread Blockers in Dynamic Networks,ustering coefficient seems to be a good indicator, while its static version performs worse than the random ranking. This provides simple practical and locally computable algorithms for identifying key blockers in a network.

Baffle

Social Network Mining with Nonparametric Relational Models,ionships between entities and it performs an interpretable cluster analysis. We demonstrate the performance of IHRMs with three social network applications. We perform community analysis on the Sampson’s monastery data and perform link analysis on the Bernard & Killworth data. Finally we apply IHRMs

Terrace

Conference proceedings 2010ers 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 c