0.25
0.5
0.75
1.25
1.5
1.75
2
Mining network data
Published on Jan 31, 20171213 Views
Related categories
Chapter list
Mining Networked Data00:00
Outline00:17
Mining network data: different dimensions01:13
Networked Data03:44
Ubiquity of Networked Data - 104:33
Ubiquity of Networked Data - 205:16
Ubiquity of Networked Data - 305:38
Ubiquity of Networked Data - 406:13
Ubiquity of Networked Data - 506:35
Ubiquity of Networked Data - 607:16
Ubiquity of Networked Data - 708:22
Ubiquity of Networked Data - 809:05
Prediction Tasks in Networked Data09:39
Structured Output Prediction Tasks in Networked Data10:34
Prediction Tasks in Networked Data - 111:18
Prediction Tasks in Networked Data - 211:58
Prediction Tasks in Networked Data - 312:15
Ranking: recommend friends (social networks)12:39
Ranking: influencers in social networks13:01
Description Tasks in Networked Data - 114:08
Description Tasks in Networked Data - 214:20
Description Tasks in Networked Data - 315:01
Description Tasks in Networked Data - 415:23
Relational mining for Discovering Changes in Evolving Networks16:00
Within-Network Inference17:15
Across-Network Inference17:29
Formalization: Graph-based - 118:00
Formalization: Graph-based - 219:02
Correlations - 121:24
Correlations - 222:29
Causes of Autocorrelation22:40
Relational Correlation - 123:22
Relational Correlation - 224:10
Estimating Relational Correlation24:59
Estimating Correlation25:16
Estimating Autocorrelation25:27
Relational AutoCorrelation - 125:46
Relational AutoCorrelation - 226:24
Data Relationship ≠ Statistical Independence26:32
Estimating Autocorrelation (continuous attrs.)27:41
Learning with Networked Data27:48
Learning Network-based Predictive Clustering Trees28:33
Measures of Network Autocorrelation29:53
Moran’s I30:36
Top down induction of NPCTs - 131:10
Top down induction of NPCTs - 231:36
Top down induction of NPCTs - 331:47
Learning with Networked Data - 131:49
Learning with Networked Data - 232:30
Basic approaches for prediction - 133:09
Basic approaches for prediction - 233:53
Collective Inference35:58
Transductive Inference - 136:04
Semi-Supervised Smoothness Assumption38:38
Transductive Inference - 238:44
Analogy38:51
Multi-type Classification from Heterogeneous Networks - 139:18
Multi-type Classification from Heterogeneous Networks - 240:31
Questions?40:49