seabet online sports betting
Report title 1: Application of graph representation learning in the field of medical image analysis
Report time:November 30, 2022Sunday afternoon2:30-3:10
Reporter: Yue Hailin
Reporting location: Computer Building313Lecture Hall
Abstract:
Graph representation learning has wide applications in the field of medical image analysis，Effective use of graph representation learning can improve the level of computer-aided diagnosis。Currently，Graph representation learning has wide applications in brain degenerative diseases and pathological image analysis。
This report is divided into three parts: First, we introduce the research status of graph representation learning in brain degenerative diseases and pathological image analysis，Then introduce one or two representative cases，Finally introduce the current challenges and my own thoughts。
About the speaker:
Yue Hailin is from the School of Computer Science, Central South University22-level doctoral student. Research seabet mobile direction: medical image analysis.
Report topic 2: Introduction to the research progress of link prediction in social networks
Report time:November 30, 2022Sunday afternoon3:10-3:50
Reporter: Zheng Kai
Reporting location: Computer Building313Lecture Hall
Abstract:
Link prediction is the most basic problem in network information mining，Through the analysis of the observed network structure and other external information，Uncover missing connections and predict possible future connections。This type of method is widely used in social network information dissemination prediction。In this report，We will introduce the research progress of link prediction in social networks。This report is divided into three parts: first, the basic concepts of social networks and link prediction are introduced，Then introduce existing research issues and related cases in social networks，Finally introduce the current challenges and future directions。
About the speaker:
Zheng Kai is from the School of Computer Science, Central South University20-level doctoral student. Research direction: bioinformatics.
Report title three: Application of hypergraph in text classification and multi-label image classification
Report time:November 30, 2022Sunday afternoon3:50-4:30
Reporter: Deng Chao
Reporting location: Computer Building313Lecture Hall
Abstract:
Hypergraph is a generalized seabet app download graph structure，Different from a simple graph where one edge contains two nodes，A hyperedge can contain any number of nodes，Used to represent higher-order associations between multiple objects。With the popularity of graph data mining,Hypergraph is also used in computer vision due to its special structure and powerful expression ability、Text classification、Biological information and other fields，And attract the attention of more and more researchers。This report is from an application perspective，First introduce the concepts related to hypergraph and hypergraph neural network，Then share the application of hypergraph in multi-label image classification and text classification，Finally summarize and expand。
About the speaker:
Deng Chao is from the School of Computer Science, Central South University22-level doctoral student. Research direction: bioinformatics.
Report topic 4: Introduction to the application of graph representation learning method based on meta-path in bioinformatics
Report time:2022November 30日Afternoon 4:30-5:10
Reporter: Li Haoyuan
Reporting location: Computer Building313Lecture Hall
Abstract: Graphs are ubiquitous data structures in the real world。Due to their ubiquity，Therefore, it is of great research seabet mobile significance to extract meaningful information from graph-structured data for the development of downstream tasks。Graph representation learning replaces hand-designed features，Can learn to encode rich information representations about graphs。It is classified in node、Link Prediction、Achieved great success in tasks such as graph classification，Receiving more and more attention in recent years。In this report，We briefly introduced the path-based graph representation learning method，And withGEEKThe framework introduces the application process of meta-path in biological heterogeneous networks as an example。Finally, the difficulties and challenges encountered by meta-pathways in bioinformatics。
About the speaker:
Li Haoyuan is from the School of Computer Science, Central South University21Ph.D. student，Current research interests include machine learning and bioinformatics。
Report Topic 5: Introduction to the Research Progress of Graph Representation Learning and Graph Classification Algorithms Based on Coding Trees
Report time:November 30, 2022Sunday afternoon5:10-5:50
Reporter: Zeng Tingfeng
Reporting location: Computer Building313Lecture Hall
seabet mobile Abstract: Coding tree based on structural entropy is a simplified data that can retain the key characteristics of the graph，This report introduces the application of coding trees in graph representation learning。It is mainly divided into three parts: first, the basic concepts of structural entropy and coding tree are introduced，Then explain the coding tree construction algorithm，Finally introduce the application based on coding tree，Mainly includes graph classification、Hierarchical pooling and text classification of graphs。
About the speaker:
Zeng Tingfeng is from the School of Computer Science, Central South University21Level Master’s student。Research direction: Application of coding trees in graph data。
