| Type of Material: | Thesis |
| Title: | Representation Learning from Time Varying Networks and its Application to Temporal Link Prediction |
| Researcher: | Mohan, Anuraj |
| Guide: | Rajeev, D |
| Department: | Department of Computer Applications |
| Publisher: | Cochin University of Science & Technology, Cochin |
| Place: | Cochin |
| Year: | 2021 |
| Language: | English |
| Subject: | Computer Science | Computer Science Interdisciplinary Applications | Engineering and Technology | Graph Attention Network | Machine Learning | Network Embedding | Network Representation Learning | Temporal Network Embedding | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer Applications, Cochin University of Science & Technology, Cochin, Cochin; 2021 |
| Fulltext: | Shodhganga |
| 000 | 00000ntm a2200000ua 4500 | |
| 001 | 456259 | |
| 003 | IN-AhILN | |
| 005 | 2024-10-08 17:24:22 | |
| 008 | __ | 241008t2021||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_456259 |
| 040 | __ | |aCUST_682022|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aMohan, Anuraj|eResearcher |
| 110 | __ | |aDepartment of Computer Applications|bCochin University of Science & Technology, Cochin|dCochin|ein|0U-0253 |
| 245 | __ | |aRepresentation Learning from Time Varying Networks and its Application to Temporal Link Prediction |
| 260 | __ | |aCochin|bCochin University of Science & Technology, Cochin|c2021 |
| 300 | __ | |a179|dDVD |
| 502 | __ | |cDepartment of Computer Applications, Cochin University of Science & Technology, Cochin, Cochin|d2021|bPhD |
| 518 | __ | |d2022|oDate of Award |
| 518 | __ | |oDate of Registration|d2017 |
| 520 | __ | |aMachine learning with graph-structured data has gained broad research interest in recent years due to the increased importance of performing network mining tasks on data from various domains. Generating efficient network representation is one important challenge in applying machine learning over network data. Recently, representation learning methods are widely used in various domains to generate low dimensional latent features from complex high dimensional data. A significant amount of research effort is made in the past to generate node representations from graph-structured data using representation learning methods. Most of these methods are only applicable to static networks and therefore cannot capture the evolving nature and temporal dynamics of time-varying networks. This research aims to develop representation learning methods for two different dimensions of time-varying networks, namely dynamic networks and temporal networks. |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Interdisciplinary Applications |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aGraph Attention Network |
| 653 | __ | |aMachine Learning |
| 653 | __ | |aNetwork Embedding |
| 653 | __ | |aNetwork Representation Learning |
| 653 | __ | |aTemporal Network Embedding |
| 700 | __ | |eGuide|aRajeev, D |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/579303|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.