graph classification a comparison study
play

Graph Classification: A Comparison Study 02/04/19 Presented by: - PowerPoint PPT Presentation

Jan 11, 2023 •10 likes •310 views

Graph Classification: A Comparison Study 02/04/19 Presented by: Camilo Muoz Juan Carrillo Graph Classification Graph Classification: A Comparison Study PAGE 2 Graph Classification Induce a mapping f(x) : X {1} given a set of

  1. Graph Classification: A Comparison Study 02/04/19 Presented by: Camilo Muñoz Juan Carrillo

  2. Graph Classification Graph Classification: A Comparison Study PAGE 2

  3. Graph Classification Induce a mapping f(x) : X → {±1} given a set of training samples Input graph Classifier Label Action Comedy Romance Sci-fi Graph Classification: A Comparison Study PAGE 3

  4. Graph Classification Kernel methods Embedding Sequential methods methods Graph Classification: A Comparison Study PAGE 4

  5. The Comparison Study ● Kernel CNN (KCNN) ● Deep Graph Kernels (DGK) ● graph2vec >> Embedding ● Multi-hop Assortativity (MHA) Graph Classification: A Comparison Study PAGE 5

  6. Motivation ● Lack of evaluation of graph similarity techniques across categories ● Lack of experimental evaluation regarding multiclass classification https://ls11-www.cs.tu-dortmund.de/staff/morris/graph kerneldatasets Graph Classification: A Comparison Study PAGE 6

  7. Kernel CNN (KCNN) Graph Classification: A Comparison Study PAGE 7

  8. Kernel CNN (KCNN) Graph Classification: A Comparison Study PAGE 8

  9. Deep Graph Kernels Graph Classification: A Comparison Study PAGE 9

  10. Deep Graph Kernels Graph Classification: A Comparison Study PAGE 10

  11. graph2vec Graph Classification: A Comparison Study PAGE 11

  12. graph2vec Graph Classification: A Comparison Study PAGE 12

  13. Multi-hop Assortativity (MHA) Graph Classification: A Comparison Study PAGE 13

  14. Multi-hop Assortativity (MHA) Graph Classification: A Comparison Study PAGE 14

  15. Experimental Evaluation ● RQ1: How can the nature of the graph data (e.g. number of nodes, average number of edges per node) impact the performance of the techniques? ● RQ2: Is there a clear difference in performance when using binary classification datasets versus using multiclass graph data? ● RQ3: Is there a technique that clearly outperforms the others in terms of performance? Graph Classification: A Comparison Study PAGE 15

  16. Microsoft datasets ● Semantic image processing ● The class for each graph corresponds to its semantic meaning. For example building, grass, tree, face, car, bicycle Graph Classification: A Comparison Study PAGE 16

  17. First-MM dataset ● First-MM stands for Flexible Skill Acquisition and Intuitive Robot Tasking for Mobile Manipulation in the Real World ● The graphs represent 3d point clouds of household objects Graph Classification: A Comparison Study PAGE 17

  18. IMDb datasets Reddit datasets ● Movie collaboration graphs ● User discussion datasets ● The class correspond to the genre of ● Binary dataset contains posts from 4 the movie such as Action, Romance, popular subreddits: IAmA, AskReddit, Comedy , and Sci-Fi TrollXChromosomes , and atheism ● Multiclass dataset contains posts from 5 subreddits: worldnews, videos, AdviceAnimals, aww , and mildlyinteresting Graph Classification: A Comparison Study PAGE 18

  19. Experimental Evaluation Graph Classification: A Comparison Study PAGE 19

  20. Experimental Setup ● Dataset preprocessing ● Code customization ● Selection of initialization parameters ● Graph transformation technique ● Graph Classification Graph Classification: A Comparison Study PAGE 20

  21. Evaluation Metrics ● Mean prediction accuracies and standard deviations ● Graph transformation runtime ● Additional storage required for transformed data Graph Classification: A Comparison Study PAGE 21

  22. Evaluation Results ● Mean prediction accuracies and standard deviations Graph Classification: A Comparison Study PAGE 22

  23. Evaluation Results Graph Classification: A Comparison Study PAGE 23

  24. Evaluation Results ● Graph transformation runtime Graph Classification: A Comparison Study PAGE 24

  25. Evaluation Results ● Additional storage required for transformed data Graph Classification: A Comparison Study PAGE 25

  26. Potential Extensions Graph Classification: A Comparison Study PAGE 26

  27. Discussion Graph Classification: A Comparison Study PAGE 27

  28. Appendix PAGE 28

  29. PAGE 29

  30. PAGE 30

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Graph Classification Classification Outline Introduction, Overview Classification using

Graph Classification Classification Outline Introduction, Overview Classification using

Graph Classification Classification Outline Introduction, Overview Classification using Graphs Graph classification Direct Product Kernel Predictive Toxicology example dataset Vertex classification Laplacian Kernel

605 views • 33 slides
Classification and Compensation Study Update SCO COPE The purpose of the study is to:

Classification and Compensation Study Update SCO COPE The purpose of the study is to:

BOARD MEETING June 2, 2020 Classification and Compensation Study Update SCO COPE The purpose of the study is to: Review the current classification structure Provide appropriate classification recommendations Prepare new or revised

278 views • 8 slides
Making Set-valued Predictions in Evidential Classification: A Comparison of Different Approaches

Making Set-valued Predictions in Evidential Classification: A Comparison of Different Approaches

Making Set-valued Predictions in Evidential Classification: A Comparison of Different Approaches Liyao Ma & Thierry Denux ISIPTA 2019 - 5th July 1 Introduction Classification : label predictions = { 1 , , n }

387 views • 13 slides
Deep Learning for Classification CS293S, Yang, 2017 Computational graph for classification w 1 f

Deep Learning for Classification CS293S, Yang, 2017 Computational graph for classification w 1 f

Deep Learning for Classification CS293S, Yang, 2017 Computational graph for classification w 1 f 1 w 2 S f 2 >0? w 3 f 3 Objective: Classification Accuracy m l acc ( w ) = 1 sign( w > f ( x ( i ) )) == y ( i ) X m i =1

509 views • 49 slides
Graph Algorithms Chapter 22 1 CPTR 430 Algorithms Graph Algorithms Why Study Graph Algorithms?

Graph Algorithms Chapter 22 1 CPTR 430 Algorithms Graph Algorithms Why Study Graph Algorithms?

Graph Algorithms Chapter 22 1 CPTR 430 Algorithms Graph Algorithms Why Study Graph Algorithms? Mathematical graphs seem to be relatively specialized and abstract Why spend so much time and effort on algorithms for such an obscure area?

744 views • 47 slides
Shoestring: Graph-Based Semi- Supervised Classification with Severely Limited Labeled Data Wanyu

Shoestring: Graph-Based Semi- Supervised Classification with Severely Limited Labeled Data Wanyu

Shoestring: Graph-Based Semi- Supervised Classification with Severely Limited Labeled Data Wanyu Lin , Zhaolin Gao, Baochun Li University of Toronto Graph-Based Semi-Supervised Classification Such as graph convolutional

313 views • 9 slides
An Empirical Comparison of Automated Generation and Classification Techniques for

An Empirical Comparison of Automated Generation and Classification Techniques for

An Empirical Comparison of Automated Generation and Classification Techniques for Object-Oriented Unit Testing Marcelo dAmorim (UIUC) Carlos Pacheco (MIT) Tao Xie (NCSU) Darko Marinov (UIUC) Michael D. Ernst (MIT) Automated Software

513 views • 33 slides
Comparison of LEM2 and a Dynamic Reduct Classification Algorithm Ola Leifler olale@ida.liu.se

Comparison of LEM2 and a Dynamic Reduct Classification Algorithm Ola Leifler olale@ida.liu.se

Comparison of LEM2 and a Dynamic Reduct Classification Algorithm Ola Leifler olale@ida.liu.se IDA Comparison of LEM2 and a Dynamic Reduct Classification Algorithm p.1/90 Agenda Machine learning Comparison of LEM2 and a Dynamic Reduct

1.75k views • 153 slides
Beach Profile Presentation LA PUNTA, CONDADO 2017 First Measurement Graph and Chart Second

Beach Profile Presentation LA PUNTA, CONDADO 2017 First Measurement Graph and Chart Second

Beach Profile Presentation LA PUNTA, CONDADO 2017 First Measurement Graph and Chart Second Measurement Graph and Chart Comparison Graph 1 and 2 As shown by the comparison of the graphs, the beach gained sand and the slope started later. From

226 views • 6 slides
Outline Comparison of Flow Routing Algorithms Used in Geographic Background Information

Outline Comparison of Flow Routing Algorithms Used in Geographic Background Information

Outline Comparison of Flow Routing Algorithms Used in Geographic Background Information Systems Hypotheses Study Area Fuzzy Classification Results Conclusions Christine Lam Yongxin Deng John Wilson University of

526 views • 6 slides
An Experimental Comparison of Classification Algorithms for the Hierarchical Prediction of

An Experimental Comparison of Classification Algorithms for the Hierarchical Prediction of

An Experimental Comparison of Classification Algorithms for the Hierarchical Prediction of Protein Function Part of the BIASPROFS Project : www.cs.kent.ac.uk/projects/biasprofs Andy Secker, Alex Freitas (University of Kent) Matthew Davies,

763 views • 37 slides
Comparison of Classification Techniques in Bioinformatics Rashpal Ahluwalia, Ph.D, PE Sundar

Comparison of Classification Techniques in Bioinformatics Rashpal Ahluwalia, Ph.D, PE Sundar

Comparison of Classification Techniques in Bioinformatics Rashpal Ahluwalia, Ph.D, PE Sundar Chidambaram, MS Industrial and Management Systems Engineering West Virginia University, Morgantown, WV rashpal.ahluwalia@mail.wvu.edu

692 views • 20 slides
A comparison of selectional preference models for automatic verb classification Will Roberts and

A comparison of selectional preference models for automatic verb classification Will Roberts and

Introduction Models Results References A comparison of selectional preference models for automatic verb classification Will Roberts and Markus Egg Institut fr Anglistik und Amerikanistik Humboldt Universitt zu Berlin Sunday, 26 October,

1.4k views • 20 slides
An empirical comparison of CNNs and other methods for classification of protein subcellular

An empirical comparison of CNNs and other methods for classification of protein subcellular

An empirical comparison of CNNs and other methods for classification of protein subcellular localization with microscopy images Mengli Xiao, Wei Pan Division of Biostatistics University of Minnesota June 5, 2018 Mengli Xiao, Wei Pan (UMN)

691 views • 25 slides
Classification of normal and pathological brain networks based on similarity of graph partitions

Classification of normal and pathological brain networks based on similarity of graph partitions

Classification of normal and pathological brain networks based on similarity of graph partitions Anvar Kurmukov, Yulia Dodonova, Leonid Zhukov What is a connectome? ( connectome = brain network ) At a macroscale, connectome is a graph in which

1.45k views • 15 slides
Exploring Social Tagging Graph for Web Object Classification Zhijun Yin, Rui Li, Qiaozhu Mei,

Exploring Social Tagging Graph for Web Object Classification Zhijun Yin, Rui Li, Qiaozhu Mei,

Data e Web Mining AA 2009/2010 Exploring Social Tagging Graph for Web Object Classification Zhijun Yin, Rui Li, Qiaozhu Mei, Jiawei Han Giulia Mialich 825102 Luca Rossi 825038 Stats recently announced 300

733 views • 54 slides
CAR-T Matteo G Carrabba IRCCS Ospedale San Raffaele, Milano UO Ematologia-Trapianto Midollo

CAR-T Matteo G Carrabba IRCCS Ospedale San Raffaele, Milano UO Ematologia-Trapianto Midollo

IMMUNOTHERAPY IN HEMATOLOGICAL MALIGNANCIES, 17-19 May 2018, Cuneo CAR-T Matteo G Carrabba IRCCS Ospedale San Raffaele, Milano UO Ematologia-Trapianto Midollo Osseo Unit di Fase I Ematologia Confidential, OSR 1 Key Points of This Talk

650 views • 34 slides
FERROFLUIDS FERROFLUIDS Synthesis, structure, properties and applications Lecture prepared by

FERROFLUIDS FERROFLUIDS Synthesis, structure, properties and applications Lecture prepared by

European School of Magnetism New magnetic materials and their functions September 9-18, 2007 in Cluj-Napoca, Romania FERROFLUIDS FERROFLUIDS Synthesis, structure, properties and applications Lecture prepared by Ladislau Vks 1,2 With the

999 views • 72 slides
Toward a More Accurate Genome: Algorithms for the Analysis of High- Throughput Sequencing Data

Toward a More Accurate Genome: Algorithms for the Analysis of High- Throughput Sequencing Data

Toward a More Accurate Genome: Algorithms for the Analysis of High- Throughput Sequencing Data Dissertation Defense W. Jacob B. Biesinger Tuesday, May 27th AREM TreeHMM Applied statistics Genomix Applied computer science ChIP-seq

881 views • 75 slides
Genomic and epigenomic signatures

Genomic and epigenomic signatures

ATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA TATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC

107 views • 10 slides
Our mission is to promote knowledge and research in the fjeld of immunology worldwide. History

Our mission is to promote knowledge and research in the fjeld of immunology worldwide. History

Immunopaedia.org is an open-access online platgorm freely available for learning and teaching immunology. Targetjng clinicians and students Our mission is to promote knowledge and research in the fjeld of immunology worldwide. History

584 views • 30 slides
Great Cases From the Bay Ayesha Appa, MD Annie Luetkemeyer, MD Hyman Scott, MD Carina Marquez,

Great Cases From the Bay Ayesha Appa, MD Annie Luetkemeyer, MD Hyman Scott, MD Carina Marquez,

12/13/19 Great Cases From the Bay Ayesha Appa, MD Annie Luetkemeyer, MD Hyman Scott, MD Carina Marquez, MD Division of HIV, Infectious Diseases, and Global Medicine University of California, San Francisco Zuckerberg San Francisco General

559 views • 36 slides
33% 67% 33% 1 10/9/2018 Endocrine Aging Initiates Late Onset Alzheimers Risk

33% 67% 33% 1 10/9/2018 Endocrine Aging Initiates Late Onset Alzheimers Risk

10/9/2018 Perimenopause as a Neurologic Transition State: Emergence of Vulnerabilities to Neurodegenerative Disease 2018 North American Menopause Society San Diego, Ca October 04, 2018 Roberta Diaz Brinton, Ph.D. Center for Innovation in

584 views • 13 slides
What Diabetes Medicine Do I Use When? Elizabeth J. Murphy, MD, DPhil June 15, 2020 I have no

What Diabetes Medicine Do I Use When? Elizabeth J. Murphy, MD, DPhil June 15, 2020 I have no

What Diabetes Medicine Do I Use When? Elizabeth J. Murphy, MD, DPhil June 15, 2020 I have no financial interests or relationships to disclose. Today Overview of the advantages and disadvantages of the different classes of drugs (thinking like

885 views • 74 slides

More recommend