標(biāo)題: Titlebook: Advances in Social Network Mining and Analysis; Second International Lee Giles,Marc Smith,Haizheng Zhang Conference proceedings 2010 The Ed [打印本頁] 作者: 連續(xù)不斷 時間: 2025-3-21 18:19
書目名稱Advances in Social Network Mining and Analysis影響因子(影響力)
書目名稱Advances in Social Network Mining and Analysis影響因子(影響力)學(xué)科排名
書目名稱Advances in Social Network Mining and Analysis網(wǎng)絡(luò)公開度
書目名稱Advances in Social Network Mining and Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Social Network Mining and Analysis被引頻次
書目名稱Advances in Social Network Mining and Analysis被引頻次學(xué)科排名
書目名稱Advances in Social Network Mining and Analysis年度引用
書目名稱Advances in Social Network Mining and Analysis年度引用學(xué)科排名
書目名稱Advances in Social Network Mining and Analysis讀者反饋
書目名稱Advances in Social Network Mining and Analysis讀者反饋學(xué)科排名
作者: CBC471 時間: 2025-3-21 20:47
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作者: 和平主義者 時間: 2025-3-22 01:20 作者: Dungeon 時間: 2025-3-22 05:00 作者: 逃避系列單詞 時間: 2025-3-22 10:37
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 作者: Small-Intestine 時間: 2025-3-22 14:39 作者: 不妥協(xié) 時間: 2025-3-22 18:56
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作者: 不可救藥 時間: 2025-3-22 22:39
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.作者: Baffle 時間: 2025-3-23 04:47
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作者: Terrace 時間: 2025-3-23 09:07
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作者: 不安 時間: 2025-3-23 10:46 作者: frozen-shoulder 時間: 2025-3-23 15:14
https://doi.org/10.1007/978-3-030-13712-0 community detection. Existing algorithms, including the popular modularity-optimization methods, look for regions of the network that are better connected internally, e.g., have higher than expected number of edges within them. We believe, however, that edges do not give the true measure of network作者: Arboreal 時間: 2025-3-23 21:52 作者: 填滿 時間: 2025-3-23 23:17
Recognizing Chords with EDS: Part One,n the context of the spread of a disease or undesirable behavior, it is important to identify .: individuals that are most effective in stopping or slowing down the spread of a process through the population. This problem has so far resisted systematic algorithmic solutions. In an effort to formulat作者: 赤字 時間: 2025-3-24 02:32 作者: 無禮回復(fù) 時間: 2025-3-24 07:15
Mika Kuuskankare,Mikael Laurson work studies them in isolation. Here, we investigate how these networks can be overlaid, and propose a feature taxonomy for link prediction. We show that when there are tightly-knit family circles in a social network, we can improve the accuracy of link prediction models. This is done by making use作者: 蝕刻 時間: 2025-3-24 13:14
https://doi.org/10.1007/978-3-540-85035-9portant limitations of modularity as a community quality criterion: a resolution limit; and a bias towards finding equal-sized communities. Information-theoretic approaches that search for partitions that minimize description length are a recent alternative to modularity. This paper shows that two i作者: cataract 時間: 2025-3-24 16:23
https://doi.org/10.1007/978-3-642-14929-0blog networks; classification; community detection; computer networks; data mining; social networks作者: Benign 時間: 2025-3-24 22:56
978-3-642-14928-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE作者: 輕快走過 時間: 2025-3-24 23:59 作者: 下邊深陷 時間: 2025-3-25 07:20
Lee Giles,Marc Smith,Haizheng Zhangup-to-date results.fast-track conference proceedings.state-of-the-art report作者: 小步舞 時間: 2025-3-25 10:52 作者: SEVER 時間: 2025-3-25 11:45
0302-9743 ns 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 sub作者: MUTE 時間: 2025-3-25 19:15 作者: 抱狗不敢前 時間: 2025-3-25 23:31 作者: 陶器 時間: 2025-3-26 01:26
Mika Kuuskankare,Mikael Laursonre accurate) compared to using more traditional features such as descriptive node attributes and structural features. The experiments also show that a combination of all three types of attributes results in the best precision-recall trade-off.作者: Psa617 時間: 2025-3-26 04:19
https://doi.org/10.1007/978-3-540-85035-91) SGE does not exhibit a resolution limit on sparse graphs in which other approaches do, and that (2) modularity and the compression-based algorithms, including SGE, behave similarly on graphs not subject to the resolution limit.作者: In-Situ 時間: 2025-3-26 10:45
Community Detection Using a Measure of Global Influence,ere nodes have more influence over each other than over nodes outside the community. We evaluate our approach on several networks and show that it often outperforms the edge-based modularity algorithm.作者: 自制 時間: 2025-3-26 13:03
Using Friendship Ties and Family Circles for Link Prediction,re accurate) compared to using more traditional features such as descriptive node attributes and structural features. The experiments also show that a combination of all three types of attributes results in the best precision-recall trade-off.作者: malapropism 時間: 2025-3-26 17:48
Information Theoretic Criteria for Community Detection,1) SGE does not exhibit a resolution limit on sparse graphs in which other approaches do, and that (2) modularity and the compression-based algorithms, including SGE, behave similarly on graphs not subject to the resolution limit.作者: Spongy-Bone 時間: 2025-3-26 22:24 作者: 冰雹 時間: 2025-3-27 04:45 作者: Licentious 時間: 2025-3-27 05:22 作者: Flagging 時間: 2025-3-27 10:30 作者: 槍支 時間: 2025-3-27 15:38
Social Network Mining with Nonparametric Relational Models,ks. Infinite hidden relational models (IHRMs) introduce nonparametric mixture models into relational learning and have been successful in many relational applications. In this paper we explore the modeling and analysis of complex social networks with IHRMs for community detection, link prediction an作者: braggadocio 時間: 2025-3-27 21:12 作者: geometrician 時間: 2025-3-27 23:37
Information Theoretic Criteria for Community Detection,portant limitations of modularity as a community quality criterion: a resolution limit; and a bias towards finding equal-sized communities. Information-theoretic approaches that search for partitions that minimize description length are a recent alternative to modularity. This paper shows that two i作者: 規(guī)范就好 時間: 2025-3-28 02:34 作者: 宏偉 時間: 2025-3-28 07:31
