Network science is a rapidly emerging field of study that encompasses mathematics, computer science, physics, and engineering. A key issue in the study of complex networks is to understand the collective behavior of the various elements of these networks.
Although the results from graph theory have proven to be powerful in investigating the structures of complex networks, few books focus on the algorithmic aspects of complex network analysis. Filling this need, Complex Networks: An Algorithmic Perspective supplies the basic theoretical algorithmic and graph theoretic knowledge needed by every researcher and student of complex networks.
This book is about specifying, classifying, designing, and implementing mostly sequential and also parallel and distributed algorithms that can be used to analyze the static properties of complex networks. Providing a focused scope which consists of graph theory and algorithms for complex networks, the book identifies and describes a repertoire of algorithms that may be useful for any complex network.
Provides the basic background in terms of graph theory Supplies a survey of the key algorithms for the analysis of complex networks Presents case studies of complex networks that illustrate the implementation of algorithms in real-world networks, including protein interaction networks, social networks, and computer networks Requiring only a basic discrete mathematics and algorithms background, the book supplies guidance that is accessible to beginning researchers and students with little background in complex networks. To help beginners in the field, most of the algorithms are provided in ready-to-be-executed form.
While not a primary textbook, the author has included pedagogical features such as learning objectives, end-of-chapter summaries, and review questions
Country of Publication:
12 October 2017
BACKGROUND Introduction Overview Real-World Complex Networks Technological Networks Information Networks Social Networks Biological Networks Topological Properties of Complex Networks Algorithmic Challenges Outline of the Book References Graph Theory Basics Subgraphs Graph Isomorphism Types of Graphs Paths and Cycles Connectivity Trees Graph Representations Spectral Properties of Graphs Eigenvalues and Eigenvectors The Laplacian Matrix Chapter Notes References Algorithms and Complexity Introduction Time Complexity Recurrences Divide and Conquer Algorithms Graph Algorithms Breadth-first Search Depth-first Search Dynamic Programming Greedy Algorithms NP-Complete Problems NP Completeness Reductions Satisfiability Problems 3-SAT to Independent Set Independent Set to Vertex Cover Independent Set to Clique Coping with NP Completeness Backtracking Branch and Bound Approximation Algorithms Parallel Algorithms Architectural Constraints Example Algorithms Distributed Systems and Algorithms Chapter Notes References Analysis of Complex Networks Introduction Vertex Degrees Degree Sequence Degree Distribution Communities Clustering Coefficient The Matching Index Centrality Network Motifs Models Small World Networks Scale-Free Networks Chapter Notes References ALGORITHMS Distance and Centrality Introduction Finding Distances Average Distance Dijkstra's Single Source Shortest Paths Algorithm Floyd-Warshall All Pairs Shortest Paths Algorithm Centrality Degree Centrality A Distributed Algorithm for k-hop Degree Centrality Closeness Centrality Stress Centrality Betweenness Centrality Newman's Algorithm Brandes' Algorithm Eigenvalue Centrality Chapter Notes References Special Subgraphs Introduction Maximal Independent Sets Dominating Sets A Greedy MDS Algorithm Guha-Khuller First MCDS Algorithm Guha-Khuller Second MCDS Algorithm Matching A Maximal Unweighted Matching Algorithm A MaximalWeighted Matching Algorithm Vertex Cover A Minimal Connected Vertex Cover Algorithm A Minimal Weighted Vertex Cover Algorithm A Distributed Algorithm for MWVC Construction Chapter Notes References Data Clustering Introduction Types of Data Clustering Agglomerative Hierarchical Clustering k-means Algorithm Nearest Neighbor Algorithm Fuzzy Clustering Density-based Clustering Parallel Data Clustering Chapter Notes References Graph-based Clustering Introduction Graph Partitioning BFS-based Partitioning Kernighan-Lin Algorithm Spectral Bisection Multi-level Partitioning Parallel Partitioning Graph Clustering MST-based Clustering Clustering with Clusterheads Discovery of Dense Subgraphs Definitions Clique Algorithms The First Algorithm The Second Algorithm k-core Algorithm Chapter Notes References Network Motif Discovery Introduction Network Motifs Measures of Motif Significance Generating Null Models Hardness of Motif Discovery Subgraph Isomorphism Vertex Invariants Algorithms Ullman's Algorithm Nauty Algorithm VF2 Algorithm BM1 Algorithm Motif Discovery Algorithms Exact Census Algorithms Mf inder Algorithm Enumerate Subgraphs (ESU) Algorithm Grochow and Kellis Algorithm Kavosh Algorithm MODA Approximate Algorithms with Sampling Mf inder with Sampling Randomized ESU Algorithm MODA with Sampling Chapter Notes References APPLICATIONS Protein Interaction Networks Introduction Topological Properties of PPI Networks Detection of Protein Complexes Highly Connected Subgraphs Algorithm Restricted Neighborhood Search Algorithm Molecular Complex Detection Algorithm Markov Clustering Algorithm Network Motifs in PPI Networks Network Alignment Quality of the Alignment Topological Similarity Node Similarity Algorithms PathBLAST MaWIsh IsoRank GRAAL Recent Algorithms Chapter Notes References Social Networks Introduction Relationships Homophily Positive and Negative Relations Structural Balance Equivalence Community Detection Algorithms Edge Betweenness-based Algorithm Resistor Networks RandomWalk Centrality Modularity-based Algorithm Chapter Notes References The Internet and the Web Introduction The Internet Services Services of Connection Circuit and Packet Switching Internet Protocol Suite Analysis The Web The Web Graph Properties Models Evolving Model Copying Model Growth-deletion Model Multi-layer Model Cyber Community Detection Link Analysis Hubs and Authorities Page Rank Algorithm Chapter Notes References Ad hoc Wireless Networks Introduction Clustering Algorithms Lowest-ID Algorithm Dominating Set-based Clustering Spanning Tree-based Clustering Mobile Social Networks Architecture Community Detection Middleware MobiSoC MobiClique SAMOA Yarta Chapter Notes References Index
Kayhan Erciyes is a professor of computer science and engineering and also the rector of Izmir University, Izmir, Turkey. Dr. Erciyes worked as a research and development engineer of Alcatel Turkey, Alcatel Portugal, and Alcatel SEL. He has worked as faculty in Oregon State University, UC Davis and California State University, US and Izmir and Aegean universities. His research interests are on distributed systems, graph theory and distributed algorithms for complex networks, mobile ad hoc networks, wireless sensor networks and the Grid and has published extensively in these areas. Dr. Erciyes is the designer and implementer of one of the first commercially available MODEMs in Turkey