This chapter is from the book
This chapter is from the book
This chapter is from the book
1.3 Benefits of Studying Complex Networks
It is interesting to consider the benefits of studying complex networks. The most important benefit of applying the complex networks approach to real-world problems is to obtain a significant collection of tools to study various complex systems of the physical world. That is, modeling a real-world problem as a complex network model provides significant insights to the characteristics of the real-world problem. In general, we can find the following benefits in studying complex networks: (i) modeling and characterizing complex physical world systems, (ii) design of efficient physical world systems, (iii) development of solutions to complex real-world problems, (iv) molecular networks for biomedical research, (v) network medicine, (vi) neutralizing antisocial networks, and (vii) enhanced social science research through social networks. A brief description of various application domains where complex network modeling can be very useful is provided here.
1.3.1 Modeling and Characterizing Complex Physical World Systems
Studying and analyzing complex physical systems is not only challenging but also unavoidable in our daily life. As computing, communication, and physical systems become more and more complex, new techniques and solutions are required to model and characterize them. The volume of information exchanged as well as the diversity of devices communicating over the Internet have grown tremendously. For example, the Internet had only less than 100 nodes two decades ago. Today, it has more than 6 billion connected devices. The expansion of the Internet resulted in emergence of the Internet of Things (IoT), which consists of traditional physical objects (or items called usually as things), such as household furnitures, kitchen appliances, electrical lights fans, and other similar appliances, electromechanical devices such as automated gates, are Internet-enabled. The growth of IoT is predicted to go beyond 50 billion nodes by 2020. Due to such fast-growing complexity, analyzing the topological and other characteristics is too challenging; therefore, complex network analysis is essential to model and characterize the ever-growing Internet. Similarly, complex network analysis provides a means to model, characterize, and understand many such complex physical world systems.
1.3.2 Design of New and Efficient Physical World Systems
Studying complex networks can help create new system designs as well as improve the design, development, and deployment of various physical systems. For example, consider the design and deployment of wireless sensor networks. An efficient small-world wireless sensor network (SWWSN) system not only can improve energy savings and performance of the system but also can extend the network lifetime. Another example is the design of a small-world wireless mesh network (SWWMN), which can improve the network throughput, delay, and APL performance, thereby achieving higher network capacity. Social networks is another area where the network augmentation can be carried out for signifi-cant benefits. For example, the message propagation properties can be altered by using complex network principles in a social network. Therefore, complex network modeling and analysis can be utilized to design and deploy more efficient complex systems in many application areas of the physical world. Similarly, using the knowledge gained from complex networks, road networks in future towns and cities can be designed better.
1.3.3 Development of Solutions to Complex Real-World Problems
Characterizing complex physical systems into complex network models can be very useful for developing solutions to many real-world problems. For example, the routing problem in a complex navigation terrain can be achieved by graph theoretical solutions such as shortest path routing or all-pairs shortest path routing. Similarly, in a social network, problems such as finding the most connected social network member can be very useful in identifying how to sell advertisements and commercial promotional information. Another example is in the performance optimization of wireless or wired computer networks using complex network analysis.
1.3.4 Enhancing Biomedical Research through Molecular Network Modeling
There exist many applications for complex network analysis pertaining to biomedical engineering as well as biomedical research by modeling and analyzing molecular networks. Molecular networks are network representations of molecules within the biochemical families as well as across different biochemical families. A biochemical compound is characterized by the molecular interactions of various molecules in it. Molecular interactions are the protein-protein interactions within a biochemical compound. The term referred to as interactome represents the protein-protein interaction network (PPIN).
1.3.5 Network Medicine
Complex network analysis approaches can be used to diagnose, prevent, and cure diseases by carefully identifying the network and analysis technique [2]. For example, the protein-protein interactions network or metabolic networks can be effectively used for removing certain parts of the network, thereby achieving a cure to the disease. Another example is to use the complex network approach to study the spread of a particular disease over social networks and develop techniques to prevent disease spread.
1.3.6 Neutralizing Antisocial Networks
Analysis of social networks is an essential activity for law enforcement agencies in many developed countries. Social networks formed by antisocial elements in order to carry out subversive activities can be identified and neutralized. International terroristic networks, narcotics trading channels, human trafficking networks, and illegal mafia nexus can be identified using complex network analysis on their social networks. The most important issue in neutralizing such networks is the identification of key individuals in the antisocial networks so that a minimal set of actions can achieve the desired effect. Centrality analysis of individuals to identify the most important person in an antisocial network is often useful in neutralizing the network.
1.3.7 Enhanced Social Science Research through Social Networks
Prior to 2000, most social research was conducted through social experiments, games, or survey information collected directly from members of any particular society. The data was collected and analyzed using social network analysis. With the evolution of the Internet, WWW, and web-based social network services such as Facebook and Twitter, interaction between members of society substantially moved to the virtual world. As a result, much of the social science research done today draws on information from social networks formed over these web-based services. The data available from these sources is both vast and multidimensional. Today, most social science research depends on data from social networks. Therefore, complex network modeling and analysis of social network data become essential for deeper analysis of social networks.
