E-Book, Englisch, Band 4, 1219 Seiten
Zhou / Akan / Bellavista Complex Sciences
1. Auflage 2009
ISBN: 978-3-642-02466-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Selected Papers
E-Book, Englisch, Band 4, 1219 Seiten
ISBN: 978-3-642-02466-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book constitutes the thoroughly refereed post-conference proceedings of the First International Conference on Complex Sciences, Complex 2009, held in Shanghai, China, in February 2009. The 227 revised full papers presented together with 23 papers from five collated workshops (COART, ComplexCCS, ComplexEN, MANDYN, SPA) were carefully reviewed and selected. The papers address the following topics: theory of art and music, causality in complex systems, engineering networks, modeling and analysis of human dynamics, social physics and its applications, structure and dynamics of complex networks, complex biological systems, complex economic systens, complex social systems, complex engineering systems, as well as complex systems methods.
Autoren/Hrsg.
Weitere Infos & Material
1;Table of Contents – Part I;13
2;Table of Contents – Part II;23
3;Return Intervals Approach to Financial Fluctuations;33
3.1;1 Introduction;33
3.2;2 Distribution of Return Intervals;37
3.3;3 Memory E.ects in the Return Interval Sequence;46
3.4;4 Models;51
3.5;5 Conclusions;53
3.6;Acknowledgments;54
3.7;References;54
4;Optimization Using a New Bio-inspired Approach;69
4.1;1 Introduction;69
4.2;2 Molecular Mechanics Algorithm (MMA);70
4.3;3 Simulation;77
4.4;4 Motivation and Biological Meaning of MMA;77
4.5;5 Conclusion;80
4.6;References;81
5;Non-smooth Thermodynamic System of Sea Ice;82
5.1;1 Introduction;82
5.2;2 The Coupled 3D Thermodynamic System of Sea Ice;83
5.3;3 Properties of U3D2LTS;86
5.4;4 Optimality Conditions of U3D2LTS;90
5.5;5 Conclusions;94
5.6;Acknowledgments;95
5.7;References;95
6;Optimal Service Capacities in a Competitive Multiple-Server Queueing Environment;96
6.1;1 Introduction;96
6.2;2 A Review on the Two-Server Queueing System;97
6.3;3 The General Multiple-Server Queueing System;99
6.4;4 A Numerical Example on Three-Server Queueing System;104
6.5;5 Concluding Remarks;105
6.6;References;106
7;On the Approximation Solution of a Cellular Automaton Tra.c Flow Model and Its Relationship with Synchronized Flow;130
7.1;1 Introduction;130
7.2;2 Model;131
7.3;3 Approximation Solution;132
7.4;4 Discussion;135
7.5;5 Conclusion;138
7.6;Acknowledgments;138
7.7;References;138
8;On Scale-Free Prior Distributions and Their Applicability in Large-Scale Network Inference with Gaussian Graphical Models;140
8.1;1 Motivation;140
8.2;2 Scale-Free Priors over Network Structures;141
8.3;3 Simulation;143
8.4;4 Discussion;146
8.5;References;146
9;Organizational Structure of the Transcriptional Regulatory Network of Yeast: Periodic Genes;170
9.1;1 Introduction;170
9.2;2 Methods;171
9.3;3 Data;172
9.4;4 Results;172
9.5;5 Conclusions;176
9.6;Acknowledgments;176
9.7;References;177
10;Packet-Level Tra.c Allocation for Real-Time Streaming over Multipath Networks;179
10.1;1 Introduction;179
10.2;2 Multipath Real-Time Streaming Analysis;180
10.3;3 Tra.c Allocation: Path Weight Determination;183
10.4;4 Weighted Size-Aware Packet Distribution Algorithm;185
10.5;5 Simulation Results;187
10.6;6 Conclusions;191
10.7;References;191
11;Particle Competition in Complex Networks for Semi-supervised Classi.cation;193
11.1;1 Introduction;193
11.2;2 ModelDescription;195
11.3;3 Computer Simulations;199
11.4;4 Conclusions;202
11.5;Acknowledgements;202
11.6;References;202
12;Retail Location Choice with Complementary Goods: An Agent-Based Model;205
12.1;1 Introduction;205
12.2;2 The Model;207
12.3;3 Experiments and Results;210
12.4;4 Sensitivity Tests on the Number of Retailers;212
12.5;5 Discussion;213
12.6;6 Retail Geographical Distribution in the Twin Cities;214
12.7;7 Conclusions;216
12.8;References;216
13;Reconstructing Gene Networks from Microarray Time-Series Data via Granger Causality;226
13.1;1 Introduction;226
13.2;2 Method;227
13.3;3 Experimental Results;231
13.4;4 Conclusion;237
13.5;References;238
14;Recognition of Important Subgraphs in Collaboration Networks;240
14.1;1 Introduction;240
14.2;2 The Model;242
14.3;3 Empirical Investigations on Some Real World Collaboration Networks;244
14.4;4 Conclusion and Discussion;248
14.5;Acknowledgement;248
14.6;References;248
15;Queueing Transition of Directed Polymer in Random Media with a Defect;250
15.1;1 Introduction;250
15.2;2 Discrete Model and Numerical Results;251
15.3;3 Summary;253
15.4;Acknowledgments;253
15.5;References;253
16;Pollution Modeling and Simulation with Multi-Agent and Pretopology;255
16.1;1 Introduction;255
16.2;2 Pollution Di.usion Model and Pretopology;256
16.3;3 Simulation Results;260
16.4;4 Conclusion;260
16.5;References;261
17;Phase Transition of Active Rotators in Complex Networks;272
17.1;1 Introduction;272
17.2;2 Model System;273
17.3;3 Phase Transition in Active Rotator Model;274
17.4;4 Summary and Remarks;276
17.5;References;276
18;Personal Recommendation in User-Object Networks;277
18.1;1 Introduction;277
18.2;2 Di.usion-Based Algorithm;278
18.3;3 Two Improved Algorithms;280
18.4;4 Conclusion;282
18.5;Acknowledgement;283
18.6;References;283
19;Performance Analysis of Public Transport Systems in Nanjing Based on Network Topology;284
19.1;1 Introduction;284
19.2;2 Statistic Parameters of Network Topology;285
19.3;3 Statistic and Topological Properties of the UPTN;286
19.4;4 E.ectiveness Analysis of the UPTN Based on Topological Statistics;289
19.5;5 Synergetic Relation between Urban Rail Transit and UPTN;291
19.6;6 Conclusion;293
19.7;References;293
20;Non-sufficient Memories That Are Suffcient for Prediction;295
20.1;1 Introduction;295
20.2;2 Su.cient Statistics and Causal States;296
20.3;3 Hidden Markov Models (HMMs) and;298
20.4;Machine;298
20.5;4 Predictive Interpretation of HMMs;302
20.6;5 Summary and Discussion;305
20.7;References;306
21;Multiple Phase Transitions in the Culture Dissemination;316
21.1;1 Introduction;316
21.2;2 The Model;317
21.3;3 Simulation Results;318
21.4;4 Conclusion and Discussion;319
21.5;References;320
22;Invariance of the Hybrid System in Microbial Fermentation;332
22.1;1 Introduction;332
22.2;2 Hybrid Nonlinear Dynamical System;333
22.3;3 Stability Criteria;336
22.4;4 Conclusion;338
22.5;Acknowledgements;338
22.6;References;338
23;Inter-Profile Similarity (IPS): A Method for Semantic Analysis of Online Social Networks;350
23.1;1 Introduction;350
23.2;2 Related Work;352
23.3;3 IPS Algorithm;352
23.4;4 IPS User Study;356
23.5;5 Applying IPS in an OSN Study;357
23.6;6 Conclusions and Future Work;362
23.7;References;363
24;Inefficiency in Networks with Multiple Sources and Sinks;364
24.1;References;367
25;Impacts of Local Events on Communities and Diseases;369
25.1;1 Introduction;369
25.2;2 NetworkModel;370
25.3;3 Degree Distribution;371
25.4;4 Epidemic Spreading;375
25.5;5 Conclusions;378
25.6;Acknowledgments;378
25.7;References;378
26;Identifying Social Communities in Complex Communications for Network E.ciency;381
26.1;1 Introduction;381
26.2;2 Experimental Datasets;382
26.3;3 Communities in the Mobility Traces;383
26.4;4 Single-Point Communication;384
26.5;5 Multi-point Communication;385
26.6;6 Results and Evaluations;387
26.7;7 Conclusion;392
26.8;References;393
27;Less Restrictive Synchronization Criteria in Complex Networks with Coupling Delays;406
27.1;1 Introduction;406
27.2;2 A Complex Network Model and Necessary Preliminaries;407
27.3;3 Synchronization in Complex Networks with Symmetric Topology;408
27.4;4 Synchronization in Complex Networks with Asymmetric Topology;412
27.5;5 Numerical Simulations;415
27.6;6 Conclusion;416
27.7;References;417
28;MANIA: A Gene Network Reverse Algorithm for Compounds Mode-of-Action and Genes Interactions Inference;419
28.1;1 Introduction;419
28.2;2 Related Work;421
28.3;3 Method;422
28.4;4 Experimental Results;423
28.5;5 Discussion;427
28.6;6 Conclusion;428
28.7;References;429
29;Moving Breather Collisions in the Peyrard-Bishop DNA Model;441
29.1;1 Introduction and Model Set-Up;441
29.2;2 Results and Conclusions;443
29.3;References;446
30;Modular Synchronization in Complex Network with a Gauge Kuramoto Model;459
30.1;1 Introduction;459
30.2;2 Module Identi.cation;460
30.3;3 Detecting Algorithms in Synchronization;460
30.4;4 Kuramoto Model with Gauge Term;460
30.5;5 Simulation Results;461
30.6;6 Summary;463
30.7;References;464
31;Modification Propagation in Complex Networks;465
31.1;1 Introduction;465
31.2;2 Model;466
31.3;3 Results;468
31.4;4 Conclusions;469
31.5;References;470
32;Modelling of Population Migration to Reproduce Rank-Size Distribution of Cities in Japan;471
32.1;1 Introduction;471
32.2;2 DataAnalysis;472
32.3;3 Simulation;473
32.4;4 Simulation Result;474
32.5;5 Concluding Remarks;475
32.6;References;475
33;Modeling and Robustness Analysis of Biochemical Networks of Glycerol Metabolism by Klebsiella Pneumoniae;476
33.1;1 Introduction;476
33.2;2 Modeling and Parameter Identi.cation;478
33.3;3 Robustness Analysis;483
33.4;4 Conclusions and Discussions;486
33.5;Acknowledgements;486
33.6;References;487
34;Dynamical System of Continuous Culture;488
34.1;1 Introduction;488
34.2;2 Models and Properties;489
34.3;3 Numerical Simulation;494
34.4;4 Conclusions;495
34.5;Acknowledgements;496
34.6;References;496
35; Modeling a Complex Biological Network with Temporal Heterogeneity: Cardiac Myocyte Plasticity as a Case Study;497
35.1;1 Introduction;497
35.2;2 Approach;499
35.3;3 Modeling the Cardiac Myocyte Plasticity;501
35.4;4 In-Silico Results;508
35.5;5 Discussion;512
35.6;6 Conclusion;513
35.7;References;513
36;Measuring the E.ciency of Network Designing;533
36.1;1 Introduction;533
36.2;2 Related Work;534
36.3;3 Tra.cFlowModel;535
36.4;4 Measuring a Network Designing Strategy;536
36.5;5 Experimental Studies;537
36.6;6 Conclusion;542
36.7;References;543
37;Gravity Model for Transportation Network Based on Optimal Expected Tra.c;544
37.1;1 Introduction;544
37.2;2 Expected Tra.c and Gravity;545
37.3;3 Gravity Model for Transportation Network;546
37.4;4 Simulation for the Chinese City Airline Network;548
37.5;5 Expected Tra.c and Real Tra.c;551
37.6;6 Conclusion;553
37.7;References;553
38;A Bipartite Graph Based Model of Protein Domain Networks;555
38.1;1 Introduction;555
38.2;2 Theoretical Model and Experimental Results;556
38.3;3 Conclusion;563
38.4;References;564
39;The Probability Distribution of Inter-car Spacings;571
39.1;1 Introduction;571
39.2;2 Model and Analytical Solution of Inter-car Spacings Distribution;572
39.3;3 Simulation;575
39.4;4 Notes and Comments;577
39.5;References;578
40;The Evolution of ICT Markets: An Agent-Based Model on Complex Networks;599
40.1;1 Introduction;599
40.2;2 The Model;601
40.3;3 TheResults;602
40.4;4 Conclusions;606
40.5;5 SensitiveAnalysis;607
40.6;Acknowledgments;608
40.7;References;608
41;The Effects of Link and Node Capacity on Trafic Dynamics in Weighted Scale-Free Networks;610
41.1;1 Introduction;610
41.2;2 Tra.cModel;611
41.3;3 Simulation Results and Discussions;612
41.4;4 Conclusion;616
41.5;Acknowledgments;617
41.6;References;617
42;The Effect of Lane-Changing Time on the Dynamics of Tra.c Flow;619
42.1;1 Introduction;619
42.2;2 Model;620
42.3;3 Simulations and Discussions;623
42.4;4 Conclusion;627
42.5;Acknowledgements;627
42.6;References;627
43;The Contrast of Parametric and Nonparametric Volatility Measurement Based on Chinese Stock Market;648
43.1;1 Introduction;648
43.2;2 Realized Volatility and;649
43.3;Model;649
43.4;3 Return Standardization;652
43.5;4 Empirical Analysis;652
43.6;5 Conclusion and Directions for Future Research;656
43.7;References;657
44;The Topological Characteristics and Community Structure in Consumer-Service Bipartite Graph;670
44.1;1 Introduction;670
44.2;2 Related Research Work;671
44.3;3 Empirical Study;674
44.4;4 Conclusions;679
44.5;References;680
45;Time Dependent Virus Replication in Cell Cultures;681
45.1;1 Introduction;681
45.2;2 Model and Strategies;683
45.3;References;685
46;Visualization of Complex Biological Systems: An Immune Response Model Using OpenGL;701
46.1;1 Introduction;701
46.2;2 Research Objectives;702
46.3;3 Model-View-Controller;702
46.4;4 Visualisation Results;706
46.5;5 Summary and Conclusions;708
46.6;References;708
47;Using the Weighted Rich-Club Coeficient to Explore Tra.c Organization in Mobility Networks;710
47.1;1 Introduction;710
47.2;2 Mobility Networks: Air Transportation and Commuting Patterns;711
47.3;3 Weighted Rich-Club Coe.cient;713
47.4;4 Results;714
47.5;5 Comparison with a Simple Tra.c Model;717
47.6;6 Conclusions;720
47.7;References;720
48;Tracking the Evolution in Social Network: Methods and Results;723
48.1;1 Introduction;723
48.2;2 Notation and De.nition;724
48.3;3 Datasets;725
48.4;4 Methods, Algorithms and Experiments;726
48.5;5 Conclusion;734
48.6;References;735
49;Towards a Partitioning of the Input Space of Boolean Networks: Variable Selection Using Bagging;745
49.1;1 Introduction;745
49.2;2 Methods;746
49.3;3 Results;749
49.4;4 Conclusions;752
49.5;Acknowledgements;752
49.6;References;752
50;Toward Automatic Discovery of Malware Signature for Anti-Virus Cloud Computing;754
50.1;1 Introduction;754
50.2;2 Virus Executable File Format;755
50.3;3 AMSDS;755
50.4;4 Simulation;756
50.5;5 Conclusion;758
50.6;References;758
51;Temperature-Induced Domain Shrinking in Ising Ferromagnets Frustrated by a Long-Range Interaction;813
51.1;References;816
52;Slowdown in the Annihilation of Two Species Diffusion-Limited Reaction on Fractal Scale-Free Networks;817
52.1;1 Introduction;817
52.2;2 Two-Species Annihilation on Fractal Scale-Free Networks;818
52.3;3 Role of Local Hubs in Fractal SF Networks;819
52.4;4 Summary;820
52.5;References;820
53;SIRS Dynamics on Random Networks: Simulations and Analytical Models;822
53.1;References;827
54;Self-organized Balanced Resources in Random Networks with Transportation Bandwidths;836
54.1;1 Introduction;836
54.2;2 The Model;837
54.3;3 Analysis;837
54.4;4 Distributed Algorithms;839
54.5;5 The High Connectivity Limit;841
54.6;6 Scale-Free Networks;846
54.7;7 Conclusion;847
54.8;Acknowledgements;847
54.9;References;847
55;Selection of Imitation Strategies in Populations When to Learn or When to Replicate?;849
55.1;1 Introduction;849
55.2;2 Model;851
55.3;3 Results;854
55.4;4 Discussion;859
55.5;5 Conclusion;859
55.6;References;860
56;Sediment Transport Dynamics in River Networks: A Model for Higher-Water Seasons;862
56.1;References;869
57;Scaling Relations in Absorbing Phase Transitions with a Conserved Field in One Dimension;871
57.1;1 Introduction;871
57.2;2 Scaling Theory;873
57.3;3 Conserved Lattice Gas Model;874
57.4;4 Conserved Threshold Transfer Process;877
57.5;5 Concluding Remarks;880
57.6;Acknowledgments;881
57.7;References;881
58;Scaling Law between Urban Electrical Consumption and Population in China;883
58.1;1 Introduction;883
58.2;2 Data of Urban Population and Household Electrical Consumption;885
58.3;3 DataAnalysis;886
58.4;4 Growth of City in Di.erent Categories;890
58.5;5 Conclusions;892
58.6;Acknowledgement;893
58.7;References;893
59;Scaling in Modulated Systems;895
59.1;References;896
60;Scaling Behavior of Chinese City Size Distribution;898
60.1;1 Introduction;898
60.2;2 Data of Chinese Cities;900
60.3;3 DataAnalysis;900
60.4;4 Conclusions;903
60.5;Acknowledgement;904
60.6;References;904
61;Social Network as Double-Edged Sword to Exchange: Frictions and the Emerging of Intellectual Intermediary Service;906
61.1;1 Introduction;906
61.2;2 Frictions of Exchange Network;907
61.3;3 The Optimization of Exogenous Intermediary Service;910
61.4;4 The Optimization of Endogenous Intermediary Service;914
61.5;5 Conclusion;916
61.6;Acknowledgement;916
61.7;References;917
62;Spam Source Clustering by Constructing Spammer Network with Correlation Measure;919
62.1;References;923
63;Spiral Waves Emergence in a Cyclic Predator-Prey Model;924
63.1;1 Introduction;924
63.2;2 Model;925
63.3;3 Results;925
63.4;4 Conclusion and Discussion;928
63.5;References;929
64;Synchronization Stability of Coupled Near-Identical Oscillator Network;930
64.1;1 Introduction;930
64.2;2 Theory: Master Stability Equations and Functions;931
64.3;3 Examples of Application;936
64.4;4 Summary;940
64.5;References;941
65;Synchronization of Complex Networks with Time-Varying Coupling Delay via Impulsive Control;942
65.1;1 Introduction;942
65.2;2 Problem Formulation and Preliminaries;944
65.3;3 MainResults;945
65.4;4 Application to the Network of Coupled Lorenz Oscillators;949
65.5;5 Conclusion;952
65.6;Acknowledgments;952
65.7;References;952
66;Synchronization in Complex Networks with Different Sort of Communities;954
66.1;1 Introduction;954
66.2;2 Kuramoto Model and the Order Parameter;955
66.3;3 Communities Identi.ed by Structure;956
66.4;4 Communities Identi.ed by the Intrinsic Frequencies Probability Density;957
66.5;5 Conclusion and Discussion;962
66.6;References;962
67;Structure of Mutualistic Complex Networks;984
67.1;1 Introduction;984
67.2;2 Data of Food Web;985
67.3;3 Structure of Mutualistic Networks;985
67.4;4 Conclusions;988
67.5;References;988
68;Statistical Properties of Cell Topology and Geometry in a Tissue-Growth Model;1001
68.1;1 Introduction;1001
68.2;2 Materials and Methods;1002
68.3;3 Results;1005
68.4;4 Discussion;1006
68.5;References;1008
69;Stability of Non-diagonalizable Networks: Eigenvalue Analysis;1010
69.1;1 Introduction;1010
69.2;2 Stability of Synchronization State by Pinning Control;1011
69.3;3 Conclusions;1019
69.4;Acknowledgments;1019
69.5;References;1019
70;Global Synchronization of Generalized Complex Networks with Mixed Coupling Delays;1031
70.1;1 Introduction;1031
70.2;2 Complex Dynamical Networks Model and Preliminaries;1032
70.3;3 MainResults;1034
70.4;4 Numerical Examples;1037
70.5;5 Conclusions;1039
70.6;Acknowledgments;1039
70.7;References;1039
71;Community Division of Heterogeneous Networks;1041
71.1;1 Introduction;1041
71.2;2 Related Work;1042
71.3;3 Bipartite Modularity;1044
71.4;4 Experiments;1047
71.5;5 Conclusion;1050
71.6;References;1051
72;Asymptotic Behavior of Ruin Probability in Insurance Risk Model with Large Claims;1063
72.1;1 Introduction;1063
72.2;2 Main Result and Insurance Signi.cance;1065
72.3;3 The proof of the Main Results;1068
72.4;References;1072
73;Approaching the Linguistic Complexity;1074
73.1;1 Introduction;1074
73.2;2 Results and Discussion;1075
73.3;References;1079
74;Analysing Weighted Networks: An Approach via Maximum Flows;1123
74.1;1 Introduction;1123
74.2;2 Weighted Global Network Measures from Flow Principles;1124
74.3;3 Summary and Conclusions;1133
74.4;References;1133
75;Bifurcation Phenomena of Opinion Dynamics in Complex Networks;1176
75.1;1 Introduction;1176
75.2;2 Improved De.uant Model;1177
75.3;3 Results;1178
75.4;4 Conclusion;1182
75.5;Acknowledgments;1182
75.6;References;1182
76;Complex Networks Community Detection of Time-Varying Mobile Social Networks;1184
76.1;1 Introduction;1184
76.2;2 Related Work;1185
76.3;3 Community Detection with Time-Varying Mobility Pattern;1185
76.4;References;1189
77;Classification Based on the Optimal K-Associated Network Network;1197
77.1;1 Introduction;1197
77.2;2 The Graph-Based Model;1198
77.3;3 Non-parametric;1204
77.4;Associated;1204
77.5;Classi.er;1204
77.6;4 Experiments and Results;1205
77.7;5 Conclusions;1206
77.8;References;1207
78;Characterizing the Structural Complexity of Real-World Complex Networks;1208
78.1;1 Introduction;1208
78.2;2 Related Work;1210
78.3;3 Analyzing Structural Complexity of Real-World Complex Networks;1211
78.4;4 Summary;1218
78.5;References;1218
