Screen LibraryComplex networks X : proceedings of the 10th Conference on Complex Networks CompleNet 2019 /
This book aims to bring together researchers and practitioners working across domains and research disciplines to measure, model, and visualize complex networks. It collects the works presented at the 10th International Conference on Complex Networks (CompleNet) in Taragona, Spain, March, 2019. With...
Saved in:
| Corporate Authors: | |
|---|---|
| Group Author: | ; ; ; |
| Published: |
Springer,
|
| Publisher Address: | Cham, Switzerland : |
| Publication Dates: | [2019] |
| Literature type: | Book |
| Language: | English |
| Series: |
Springer proceedings in complexity,
|
| Subjects: | |
| Summary: |
This book aims to bring together researchers and practitioners working across domains and research disciplines to measure, model, and visualize complex networks. It collects the works presented at the 10th International Conference on Complex Networks (CompleNet) in Taragona, Spain, March, 2019. With roots in physical, information and social science, the study of complex networks provides a formal set of mathematical methods, computational tools and theories to describe, prescribe and predict dynamics and behaviors of complex systems. Despite their diversity, whether the systems are made up of physical, technological, informational, or social networks, they share many common organizing principles and thus can be studied with similar approaches. This book provides a view of the state-of-the-art in this dynamic field and covers topics such as group decision-making, brain and cellular connectivity, network controllability and resiliency, online activism, recommendation systems, and cyber security. This text will appeal to students and researchers in the field. |
| Item Description: | International conference proceedings. |
| Carrier Form: | x, 183 pages : illustrations (some color) ; 24 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9783030144586 3030144585 9783030144609 3030144607 9783030144616 3030144615 |
| Index Number: | TK5105 |
| CLC: | TP393-532 |
| Call Number: | TP393-532/C737/2019 |
| Contents: |
Intro; Preface; Contents; Contributors; Part I Network Theory; Observability of Dynamical Networks from Graphic and Symbolic Approaches; 1 Introduction; 2 Theoretical Background; 2.1 Observability Matrix; 2.2 Symbolic Observability Coefficients; 2.3 Selecting the Variables to Measure; 3 Observability of the Node Dynamics; 4 Observability of Small Network Motifs; 4.1 Dyads (N=2); 4.2 Triads (N=3); 5 Larger Networks; 5.1 Star Network; 5.2 Ring Network; 5.3 Random Network (N=28); 6 Conclusion; References; Exploratory Factor Analysis of Graphical Features for Link Prediction in Social Networks 1 Introduction2 Related Work; 2.1 Link Prediction; 2.2 Applied Factor Analysis; 2.3 Factor Analysis in Computing; 3 Methodology; 3.1 Link-Prediction Graphical Features; 3.2 Factor Analysis; 4 Experimental Setup, Results, and Discussion; 5 Implications; 6 Conclusion and Limitations; References; An Efficient Approach for Counting Occurring Induced Subgraphs; 1 Introduction; 2 Background; 2.1 Notation; 2.2 Problem Definition; 2.3 Related Work; 3 Proposed Methodology; 3.1 Overview; 3.2 Counting Sets of Subgraphs; 3.3 Blacklisting Subgraphs; 3.4 Candidate Generation; 4 Experimental Results 5 ConclusionReferences; Part II Network Models; A Generalized Configuration Model with Degree Correlations; 1 Introduction; 2 Construction of a Random Network; 3 Joint Distribution of Degrees; 4 Assortativity and Disassortativity; 5 Numerical and Simulation Results; 6 Conclusions; References; Missing Data Augmentation for Bayesian Exponential Random Multi-Graph Models; 1 Introduction; 2 Bayesian ERmGMs; 2.1 Bayesian Inference for ERGMs; 2.2 Multiplexity; 2.3 Posterior Parameter Estimation for BERmGMs; 2.4 Cross Network Effects; 3 Missing Data Imputation; 4 Illustration: Florentine Families 5 DiscussionReferences; Part III Processes on Networks; Spread and Control of Misinformation with Heterogeneous Agents; 1 Introduction; 2 The Model; 2.1 The Re-sorting Policy; 3 Results; 3.1 Social Media with Homogeneous Agents; 3.2 The Case of Malicious Agents; 3.3 Social Media with Heterogeneous Agents; 4 Conclusion; References; Long-Term Behavior in Evolutionary Dynamics from Ergodicity Breaking; 1 Introduction and Motivation; 2 Anomalous Evolutionarily Stable States; 3 Deterministic Payoff Fluctuations; 4 Discussion; References Combining Path-Constrained Random Walks to Recover Link Weights in Heterogeneous Information Networks1 Introduction; 2 Preliminary Concepts; 3 Method; 3.1 Path-Constrained Random Walk; 3.2 Linear Regression Model; 3.3 Forward Selection Procedure; 4 Experiments; 4.1 Dataset Description and Setup; 4.2 Results; 4.2.1 Meta Paths of Length 2; 4.2.2 Importance of Meta Path Length; 4.2.3 Forward Linear Regression for Description; 4.2.4 Forward Linear Regression for Recovery Task; 5 Related Work; 6 Conclusion and Perspectives; References; Part IV Applications |