Danai Koutra – författare & böcker

Individual and Collective Graph Mining

Principles, Algorithms, and Applications

Häftad, Engelska, 2017

657 kr

Skickas inom 10-15 vardagar

Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company?This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas:Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities.Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity.The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science,to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.

Individual and Collective Graph Mining

Principles, Algorithms, and Applications

AvChristos Faloutsos,Danai Koutra

E-bok

PDF, Engelska, 2022

840 kr

Läs direkt efter köp

Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company?

This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas:

Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities.Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity.The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science,to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.

Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi - Machine Learning and Knowledge Discovery in Databases: Research Track, Häftad

Del 14169 - Lecture Notes in Computer Science

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part I

AvDanai Koutra,Claudia Plantm. fl.

Häftad, Engelska, 2023

1 229 kr

Skickas inom 5-8 vardagar

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Francesco Bonchi, Elena Baralis, Manuel Gomez Rodriguez, Claudia Plant, Danai Koutra - Machine Learning and Knowledge Discovery in Databases: Research Track, E-bok

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part I

AvFrancesco Bonchi,Elena Baralism. fl.

E-bok

Engelska, 2023

1 579 kr

Läs direkt efter köp

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.

The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.

The volumes are organized in topical sections as follows:

Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.

Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.

Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.

Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.

Part V: Robustness; Time Series; Transfer and Multitask Learning.

Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.

Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Del 14170 - Lecture Notes in Computer Science

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II

AvDanai Koutra,Claudia Plantm. fl.

Häftad, Engelska, 2023

1 235 kr

Skickas inom 10-15 vardagar

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II

AvFrancesco Bonchi,Elena Baralism. fl.

E-bok

Engelska, 2023

1 565 kr

Läs direkt efter köp

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.

The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.

The volumes are organized in topical sections as follows:

Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.

Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.

Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.

Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.

Part V: Robustness; Time Series; Transfer and Multitask Learning.

Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.

Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Del 14171 - Lecture Notes in Computer Science

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III

AvDanai Koutra,Claudia Plantm. fl.

Häftad, Engelska, 2023

1 101 kr

Skickas inom 10-15 vardagar

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.Chapter 41 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink.

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III

AvFrancesco Bonchi,Elena Baralism. fl.

E-bok

Engelska, 2023

1 329 kr

Läs direkt efter köp

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.

The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.

The volumes are organized in topical sections as follows:

Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.

Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.

Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.

Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.

Part V: Robustness; Time Series; Transfer and Multitask Learning.

Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.

Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.Chapter 41 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink.

Del 14172 - Lecture Notes in Computer Science

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part IV

AvDanai Koutra,Claudia Plantm. fl.

Häftad, Engelska, 2023

1 229 kr

Skickas inom 5-8 vardagar

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part IV

AvFrancesco Bonchi,Elena Baralism. fl.

E-bok

Engelska, 2023

1 565 kr

Läs direkt efter köp

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.

The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.

The volumes are organized in topical sections as follows:

Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.

Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.

Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.

Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.

Part V: Robustness; Time Series; Transfer and Multitask Learning.

Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.

Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Del 14173 - Lecture Notes in Computer Science

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V

AvDanai Koutra,Claudia Plantm. fl.

Häftad, Engelska, 2023

785 kr

Skickas inom 5-8 vardagar

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V

AvFrancesco Bonchi,Elena Baralism. fl.

E-bok

Engelska, 2023

944 kr

Läs direkt efter köp

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.

The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.

The volumes are organized in topical sections as follows:

Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.

Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.

Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.

Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.

Part V: Robustness; Time Series; Transfer and Multitask Learning.

Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.

Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

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Danai Koutra – författare

Individual and Collective Graph Mining

Individual and Collective Graph Mining

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

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