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Author: Tejas Puranik
Publisher:
ISBN:
Category :
Languages : en
Pages : 117
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Book Description
Most social networks are characterized by the presence of community structure, viz. the existence of clusters of nodes with a much higher proportion of links within the clusters than between the clusters. Community detection has many applications in many kinds of networks, including social networks and biological networks. Many different approaches have been proposed to solve the problem. An approach that has been shown to scale well to large networks is the Louvain method, based on maximizing modularity, which is a quality function of a partition of the nodes.In this thesis, we address the problem of community detection in evolving social networks. As social networks evolve, the community structure of the network can change. How can the community structure be updated in an efficient way? How often should community structure be updated? In this thesis, we give two methods based on the Louvain algorithm, to determine when to update the community structure. The first method, called the Edge-Distribution-Analysis algorithm, analyzes the newly added edges in order to make this decision. The second method, called the Modularity-Change-Rate algorithm, finds the rate of modularity change in a given network, and uses it to predict whether an update is required.Due to the sparsity of real datasets of evolving networks, we propose three models to generate evolving networks: a Random model, a model based on the well-known phenomenon of homophily in social networks, and another based on the phenomenon of triadic and cyclic closure. Starting with real-world data sets, we used these models to generate evolving networks. We evaluated the Edge-Distribution-Analysis algorithm and Modularity-Change-Rate algorithm on these data sets. Our results show that both our methods predict quite well when the community structure should be updated. They result in significant computational savings compared to approaches that would update the community structure after a fixed number of edge additions, while ensuring that the quality of the community structure is comparable.
Author: Tejas Puranik
Publisher:
ISBN:
Category :
Languages : en
Pages : 117
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Book Description
Most social networks are characterized by the presence of community structure, viz. the existence of clusters of nodes with a much higher proportion of links within the clusters than between the clusters. Community detection has many applications in many kinds of networks, including social networks and biological networks. Many different approaches have been proposed to solve the problem. An approach that has been shown to scale well to large networks is the Louvain method, based on maximizing modularity, which is a quality function of a partition of the nodes.In this thesis, we address the problem of community detection in evolving social networks. As social networks evolve, the community structure of the network can change. How can the community structure be updated in an efficient way? How often should community structure be updated? In this thesis, we give two methods based on the Louvain algorithm, to determine when to update the community structure. The first method, called the Edge-Distribution-Analysis algorithm, analyzes the newly added edges in order to make this decision. The second method, called the Modularity-Change-Rate algorithm, finds the rate of modularity change in a given network, and uses it to predict whether an update is required.Due to the sparsity of real datasets of evolving networks, we propose three models to generate evolving networks: a Random model, a model based on the well-known phenomenon of homophily in social networks, and another based on the phenomenon of triadic and cyclic closure. Starting with real-world data sets, we used these models to generate evolving networks. We evaluated the Edge-Distribution-Analysis algorithm and Modularity-Change-Rate algorithm on these data sets. Our results show that both our methods predict quite well when the community structure should be updated. They result in significant computational savings compared to approaches that would update the community structure after a fixed number of edge additions, while ensuring that the quality of the community structure is comparable.
Author: Rokia Missaoui
Publisher: Springer
ISBN: 331912188X
Category : Computers
Languages : en
Pages : 282
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Book Description
This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edited work will appeal to researchers, practitioners and students interested in the latest developments of social network analysis.
Author: Anirban Mondal
Publisher: Springer
ISBN: 3030047806
Category : Computers
Languages : en
Pages : 429
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Book Description
This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India, in December 2018. The 29 papers presented in this volume were carefully reviewed and selected from 93 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; financial data analytics and data streams; web and social media data; big data systems and frameworks; predictive analytics in healthcare and agricultural domains; and machine learning and pattern mining.
Author: I. Moutidis
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Robert Bembenik
Publisher: Springer
ISBN: 3319776045
Category : Technology & Engineering
Languages : en
Pages : 370
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Book Description
The inspiration for this book came from the Industrial Session of the ISMIS 2017 Conference in Warsaw. It covers numerous applications of intelligent technologies in various branches of the industry. Intelligent computational methods and big data foster innovation and enable the industry to overcome technological limitations and explore the new frontiers. Therefore it is necessary for scientists and practitioners to cooperate and inspire each other, and use the latest research findings to create new designs and products. As such, the contributions cover solutions to the problems experienced by practitioners in the areas of artificial intelligence, complex systems, data mining, medical applications and bioinformatics, as well as multimedia- and text processing. Further, the book shows new directions for cooperation between science and industry and facilitates efficient transfer of knowledge in the area of intelligent information systems.
Author: Naoki Masuda
Publisher: World Scientific
ISBN: 1786349175
Category : Science
Languages : en
Pages : 300
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Book Description
Network science offers a powerful language to represent and study complex systems composed of interacting elements — from the Internet to social and biological systems. A Guide to Temporal Networks presents recent theoretical and modelling progress in the emerging field of temporally varying networks and provides connections between the different areas of knowledge required to address this multi-disciplinary subject. After an introduction to key concepts on networks and stochastic dynamics, the authors guide the reader through a coherent selection of mathematical and computational tools for network dynamics. Perfect for students and professionals, this book is a gateway to an active field of research developing between the disciplines of applied mathematics, physics and computer science, with applications in others including social sciences, neuroscience and biology.This second edition extensively expands upon the coverage of the first edition as the authors expertly present recent theoretical and modelling progress in the emerging field of temporal networks, providing the keys to (and connections between) the different areas of knowledge required to address this multi-disciplinary problem.
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: Lasse Kliemann
Publisher: Springer
ISBN: 3319494872
Category : Computers
Languages : en
Pages : 428
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Book Description
Algorithm Engineering is a methodology for algorithmic research that combines theory with implementation and experimentation in order to obtain better algorithms with high practical impact. Traditionally, the study of algorithms was dominated by mathematical (worst-case) analysis. In Algorithm Engineering, algorithms are also implemented and experiments conducted in a systematic way, sometimes resembling the experimentation processes known from fields such as biology, chemistry, or physics. This helps in counteracting an otherwise growing gap between theory and practice.
Author: Neda Zarayeneh
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 0
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Book Description
Dynamic networks are pervasive in many applications, such as social networks and biological networks. These networks often are characterized by natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities) with a higher fraction of links within the communities than between the communities. This dissertation presents detecting communities in time-evolving dynamic networks, a significant operation used in many real-world network science applications. While there have been several proposed strategies for dynamic community detection, such approaches do not necessarily take advantage of the locality of changes. This work presents a new technique called Delta-Screening (or simply, [delta]-screening) for updating communities in a dynamic graph. The technique assumes that the graph is given as a series of time steps and outputs a set of communities for each time step. At the start of each time step, the [delta]-screening technique examines all changes (edge additions/deletions). It computes a subset of vertices likely to be impacted by the change (using the modularity objective). Subsequently, only the identified subsets are processed for community state updates. Despite the ability of the [delta]-screening scheme to prune vertices aggressively, experiments demonstrate that this scheme generates significant savings in runtime performance (up to 38x speedup over static baseline and 5x over dynamic baseline implementations), without compromising quality. We test on both real-world and synthetic network inputs containing both edge additions and deletions. The [delta]-screening technique is generic to be incorporated into any existing modularity-optimizing clustering algorithms. We tested using two state-of-the-art clustering implementations, namely, Louvain and SLM. In addition, we also show how to use the [delta]-screening approach to delineate appropriate intervals of temporal resolutions at which to analyze a given input network.We introduce a schema using the [delta]-screening approach to track the lifespan of temporal communities, which has the flexibility to account for varying community compositions, merging, and splitting behaviors within dynamically evolving networks, and applied our method to chemical networks. When applied to a complex chemical system whose varying chemical environments cause multiple timescale behavior, [delta]-screening resolves the hierarchical timescales of temporal communities.
Author: Cheng Yang
Publisher: Morgan & Claypool Publishers
ISBN: 1636390455
Category : Computers
Languages : en
Pages : 244
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Book Description
This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE). It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions. Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.
