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Author: Meghana Venkata Palukuri
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In a network, the problem of community detection refers to finding groups of nodes and edges that form ‘communities’ relevant to the field, such as groups of people with common interests in social networks and fraudulent websites linked to each other on the web. Community detection also yields downstream use-cases such as the summarization of massive networks into smaller networks of communities. We are most interested in mining protein complexes, i.e., communities of interacting proteins, accelerating biological experiments by providing candidates for previously unknown protein complexes. Characterization of protein complexes is important, as they play essential roles in cellular functions and their disruption often leads to disease. Previous methods in community detection comprise a majority of unsupervised graph clustering strategies, which work on the assumption that communities are dense subgraphs in a network - which is not always true. Also, many community detection algorithms are in-memory and serial and do not scale to large networks. In this dissertation, we use knowledge from communities, including rich features from graph nodes, with supervised and reinforcement learning, improving on accuracies, with parallel algorithms ensuring high performance and scalability. Specifically, we work on (1) learning a community fitness function using supervised machine learning methods with AutoML; (2) a distributed algorithm for finding candidate communities using multiple heuristics; (3) learning to walk trajectories on a network leading to communities with reinforcement learning and (4) feature augmentation with graph node information, such as images and additional graph node embeddings. While we optimize our algorithms on protein complexes that have characteristics such as being overlapping in nature with different topologies, our methods are generalizable to other domains since they learn and use characteristics of communities to predict new communities. Further, in domains with limited known information, the algorithms we develop can be applied by transferring learned knowledge such as dense community fitness functions from other domains. In conclusion, we build Super.Complex, RL complex detection, and DeepSLICEM - three accurate, efficient, scalable, and generalizable community detection algorithms, that effectively utilize known community information with different machine learning methods and also present 3 evaluation measures for accurate community evaluation
Author: Meghana Venkata Palukuri
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In a network, the problem of community detection refers to finding groups of nodes and edges that form ‘communities’ relevant to the field, such as groups of people with common interests in social networks and fraudulent websites linked to each other on the web. Community detection also yields downstream use-cases such as the summarization of massive networks into smaller networks of communities. We are most interested in mining protein complexes, i.e., communities of interacting proteins, accelerating biological experiments by providing candidates for previously unknown protein complexes. Characterization of protein complexes is important, as they play essential roles in cellular functions and their disruption often leads to disease. Previous methods in community detection comprise a majority of unsupervised graph clustering strategies, which work on the assumption that communities are dense subgraphs in a network - which is not always true. Also, many community detection algorithms are in-memory and serial and do not scale to large networks. In this dissertation, we use knowledge from communities, including rich features from graph nodes, with supervised and reinforcement learning, improving on accuracies, with parallel algorithms ensuring high performance and scalability. Specifically, we work on (1) learning a community fitness function using supervised machine learning methods with AutoML; (2) a distributed algorithm for finding candidate communities using multiple heuristics; (3) learning to walk trajectories on a network leading to communities with reinforcement learning and (4) feature augmentation with graph node information, such as images and additional graph node embeddings. While we optimize our algorithms on protein complexes that have characteristics such as being overlapping in nature with different topologies, our methods are generalizable to other domains since they learn and use characteristics of communities to predict new communities. Further, in domains with limited known information, the algorithms we develop can be applied by transferring learned knowledge such as dense community fitness functions from other domains. In conclusion, we build Super.Complex, RL complex detection, and DeepSLICEM - three accurate, efficient, scalable, and generalizable community detection algorithms, that effectively utilize known community information with different machine learning methods and also present 3 evaluation measures for accurate community evaluation
Author: Tejaswini Narayanan
Publisher:
ISBN: 9781303251108
Category :
Languages : en
Pages : 105
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Book Description
Community Detection is an interesting computational technique for the analysis of networks. This technique can yield useful insights into the structural organization of a network, and can serve as a basis for understanding the correspondence between structure and function (specific to the domain of the network). In this dissertation, I have sought to leverage this technique for the study of biological networks of practical relevance and significance. The study begins with an exploration of existing techniques for Community Detection, following which an optimization is proposed for one of the widely used graph-theoretic approaches. As the next step, an investigation is performed on the suitability of a machine-learning based algorithm for Community Detection in the context of biological networks. Subsequently, the use of Community Detection for understanding pathology with a specific focus on Duchenne Muscular Dystrophy (DMD), is explored. This illustrated key distinguishing features in the structural and functional organization of the constituent biological pathways as it relates to DMD. Finally, a novel algorithm for Community Detection is proposed, which is motivated by a physical systems analogy. An analysis of the algorithm's properties, together with its applications to biological networks, is also presented. I believe that the techniques and algorithms developed as part of this dissertation in the context of biological networks, have the potential to open up new vistas for therapeutic applications such as targeted drug development.
Author: Sucheta Soundarajan
Publisher:
ISBN:
Category :
Languages : en
Pages : 147
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Book Description
Within the broad area of social network analysis research, the study of communities has become an important and popular topic. However, there is little consensus within the field regarding the structure of communities, and the research literature contains dozens of competing community detection algorithms and community evaluation metrics. In this dissertation, we present several connected contributions, each related to the general theme of communities in social networks. First, in order to motivate the study of communities in general, as well as the work later in this dissertation, we present an application of community detection methods to the link prediction problem, in which one attempts to predict which edges in an incomplete network dataset are most likely to exist in the complete network dataset. We demonstrate that use of community membership information can improve the accuracy of various simple link prediction methods, sometimes by a large margin. Next, we examine the structure of "real" annotated communities and present a novel community detection method. In this chapter, we study real networks, each containing metadata that allow us to identify "real" communities (e.g., all graduate students in the same department). We study details of these communities' structures and, based on these results, create and evaluate an algorithm for finding overlapping communities in networks. We show that this method outperforms other state-of-the-art community detection methods. Finally, we present two related sections. In the first of these two chapters, we describe the Community Structure Analysis Framework (CSAF), a machinelearning-based method for comparing and studying the structures and features of communities produced through different methods. The CSAF allows a practitioner to select a community detection method best suited for his or her application needs, and allows a researcher to better understand the behavior of different community detection algorithms. In the second of these chapters, we apply the CSAF to a variety of network datasets from different domains, and use it to obtain interesting results about the structures of communities identified algorithmically as well as through metadata annotation.
Author: Emmanuel Abbe
Publisher: Foundations and Trends (R) in Communications and Information Theory
ISBN: 9781680834765
Category :
Languages : en
Pages : 172
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Book Description
This self-contained, compact monograph is an invaluable introduction to the field of Community Detection for researchers and students working in Machine Learning, Data Science and Information Theory.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309496098
Category : Computers
Languages : en
Pages : 83
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Book Description
The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.
Author: Zachary Boyd
Publisher:
ISBN:
Category :
Languages : en
Pages : 104
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Book Description
In recent years, a massive expansion in the amount of available network data in fields such as social networks, food networks in ecology, similarity networks in machine learning, transportation networks, brain networks, and many others has motivated the development of "network science'' to describe all this data. One of the fundamental branches in network science is "community detection,'' or the decomposition of large networks into coherent subnetworks, which is useful for visualization, data exploration, hypothesis formation, approximation of network dynamics, link prediction, and a host of other tasks. Two of the most well-known frameworks for community detection are modularity optimization and stochastic block modeling. They can often uncover meaningful community structure in networks from diverse applications. However, both of these approaches are computationally demanding. In this dissertation, I will show how these two statistically-motivated frameworks for community detection can be reinterpreted more geometrically using the language of graph total variation (TV) and (discretized) surface tension, respectively. This change in perspective allows one to leverage algorithms and analytical tools developed for other problems in the fields of compressed sensing, materials science, and nonlinear partial differential equations. One also can adapt arguments from other domains to obtain theoretical guarantees on the performance of these algorithms. I illustrate these approaches on a number of synthetic and real-world datasets, yielding results competitive with other state-of-the-art techniques on problems from machine learning, image processing, social networks, and biological networks.
Author: Patrick Doreian
Publisher: John Wiley & Sons
ISBN: 1119224705
Category : Mathematics
Languages : en
Pages : 425
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Book Description
Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling. Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more. Offers a clear and insightful look at the state of the art in network clustering and blockmodeling Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively Written by leading contributors in the field of spatial networks analysis Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.
Author: Tiago P. Peixoto
Publisher: Cambridge University Press
ISBN: 1009121456
Category : Science
Languages : en
Pages : 146
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Book Description
Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This Element closes the gap between the state-of-the-art in community detection on networks and the methods actually used in practice.
Author: Thiago Christiano Silva
Publisher: Springer
ISBN: 3319172905
Category : Computers
Languages : en
Pages : 345
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Book Description
This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.
Author: Huan Liu
Publisher: Springer Science & Business Media
ISBN: 144190056X
Category : Computers
Languages : en
Pages : 275
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Book Description
Social computing is concerned with the study of social behavior and social c- text based on computational systems. Behavioral modeling reproduces the social behavior, and allows for experimenting, scenario planning, and deep understa- ing of behavior, patterns, and potential outcomes. The pervasive use of computer and Internet technologies provides an unprecedented environment of various - cial activities. Social computing facilitates behavioral modeling in model building, analysis, pattern mining, and prediction. Numerous interdisciplinary and inter- pendent systems are created and used to represent the various social and physical systems for investigating the interactions between groups, communities, or nati- states. This requires joint efforts to take advantage of the state-of-the-art research from multiple disciplines, social computing, and behavioral modeling in order to document lessons learned and develop novel theories, experiments, and methodo- gies in terms of social, physical, psychological, and governmental mechanisms. The goal is to enable us to experiment, create, and recreate an operational environment with a better understanding of the contributions from each individual discipline, forging joint interdisciplinary efforts. This is the second international workshop on Social Computing, Behavioral ModelingandPrediction. The submissions were from Asia, Australia, Europe, and America. Since SBP09 is a single-track workshop, we could not accept all the good submissions. The accepted papers cover a wide range of interesting topics.
