Research Session 11 : Data Mining and Knowledge Discovery 1
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0:00:15 [Paper: TransN: Heterogeneous Network Representation Learning by Translating Node Embeddings]
0:00:42 Agenda
0:01:25 Introduction : Heterogeneous Graphs
0:02:21 Introduction : Problem Definition
0:02:47 Introduction : Motivation (1)
0:04:30 Introduction : Motivation (2)
0:04:45 Introduction : Motivation (3)
0:06:12 TransN : Framework
0:06:49 TransN : Single-View Algorithm (1)
0:07:28 TransN : Single-View Algorithm (2)
0:08:21 TransN : Single-View Algorithm (3)
0:09:15 TransN : Cross-View Algorithm (1)
0:09:43 TransN : Cross-View Algorithm (2)
0:11:02 TransN : Cross-View Algorithm (3)
0:11:18 TransN : Complexity Analysis
0:11:49 Experiments : Settings
0:12:44 Experiments: Results (1)
0:12:55 Experiments: Results (2)
0:14:24 Conclusion
0:14:56 THANK YOU!
0:15:48 [Talk: An Adaptive Master-Slave Regularized Model for Unexpected Revenue Prediction Enhanced withAlternative Data]
0:16:16 Content
0:17:19 Background
0:20:48 Method
0:21:36 Master Model
0:22:50 Overall Framework
0:26:16 Alternative Dataset
0:27:03 Experiments
0:29:19 Conclusion
0:31:49 [Talk: Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution]
0:48:53 [Talk: Statistical Estimation of Diffusion Network Topologies]
0:49:30 Background
0:50:42 Motivation
0:52:15 Problem Statement
0:54:57 TENDS Algorithm: Overview
0:56:05 TENDS Algorithm: Details
0:59:12 TENDS Algorithm: Analysis
1:01:07 Experiment Settings
1:01:57 Experimental Evaluation
1:03:24 Takeaway
1:06:09 [Talk: Multiple Dense Subtensor Estimation with High Density Guarantee]
1:06:30 Generated,
Estimated Data
1:07:36 Structured Data Type
1:08:26 Given two contexts on the
right, which one is attack?
1:09:53 Applications
1:11:11 Extensive Study of Finding Dense Subgraphs (Subtensor)
1:11:41 The General Densest-Subgraph(Subtensor) Problem
1:13:46 Our Method
1:14:46 Contributions
1:15:24 Experiments & Evaluation
1:16:48 Some of the Results
1:18:29 Runtime
1:19:37 Conclusion
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