3.1 Introduction

Human beings have been part of social networks since our earliest days. We are born and live in a world of connections. People connect with others through social networks formed by kinship, language, trade, exchange, conflict, citation, and collaboration. Using computer technologies to create social networks is relatively new, but networks of social interactions and exchanges are primordial. Simply defined, a network is a collection of things and their relationships to one another. The “things” that are connected are called nodes, vertices, entities, and in some contexts people. The connections between the vertices are called edges, ties, relationships or links. Many natural and artificial systems form networks, which exist in systems from the atomic level to the planetary level. A special subset of networks are social networks which are created whenever people interact, directly or indirectly, with other people, institutions, and artifacts. Social network theory and analysis is a relatively recent set of ideas and methods largely developed over the past century. It builds on and uses concepts from the mathematics of graph theory, which has a longer history, starting with Leonhard Euler in 1736. Using network analysis, you can visualize complex sets of relationships as maps (i.e., graphs or sociograms) of connected symbols and calculate precise measures of the size, shape, and density of the network as a whole and the positions of each element and group of elements within it.

The recent proliferation of Internet social media applications and smartphone devices has made social connections more visible than ever before (Chapter 2). A new subset of social networks, social media networks, are a growing focal point for the application of network analysis tools. The idea of networks, whether they are composed of friends, ideas, or web pages, is increasingly an important way to think about the modern world. You can use social network analysis to explore and visualize patterns found within collections of linked entities that include people. From a social network analysis perspective, the treelike “org-chart” that commonly represents the hierarchical structure of an organization or enterprise is too simple and lacks important information about the cross connections that exist between and across departments and divisions. In contrast with the simplified tree structure of an org-chart, a social network view of an organization or population leads to the creation of visualizations that resemble maps of highway systems, airline routes, or rail networks (see Chapter 9). Social network maps can similarly guide journeys through social landscapes and tell a story about how some points or people are at the center or periphery of the network. Maps of transportation networks where distance is measured in number of flights or road miles from one city to another city are familiar. They inspire application to less familiar networks of electrical connections, protein expression, and webs of information, conversation, and human connection.

Social network analysis and metrics are described in several excellent books and journals [1–6]. This chapter touches on the key historical developments, ideas, and concepts in social network analysis and applies them to social media network examples. We have left details of advanced topics and mathematical definitions of various concepts to the many fine technical works. The following is intended as an introductory survey of the core network concepts and methods used in subsequent chapters, which focus on the networks that can be extracted from social media sources like Twitter, Facebook, email, discussion forums, YouTube, and wikis.

3.2 The network perspective

Network analysts see the world as a collection of interconnected pieces. Those studying social networks see relationships as the building blocks of the social world, each set of relationships combining to create emergent patterns of connections among people, groups, and things. The focus of social network analysis is between, not within, people. Whereas traditional social science research methods such as surveys focus on individuals and their attributes (e.g., gender, age, income), network scientists focus on individuals and their “alters”—the people to whom they connect. Network analysis shifts the focus of analysis to the bonds between individuals in addition to the internal qualities and abilities of individuals. This change in focus from attribute data to relational data dramatically affects how data are collected, represented, and analyzed. Social network analysis complements methods that focus more narrowly on individuals, adding a critical dimension that captures the connective tissue of societies and other complex interconnections.

Network analysis shares some core ideas with the real estate profession. In contrast to approaches that look at internal attributes of each individual, network analysis shares the real estate focus on location, location, location! The interior of a house may be a liability, but where a property is located matters far more when trying to get a good sale price. The network perspective looks at a collection of ties among a population and creates measurements that describe the location of each person or entity within the structure of all relationships in the network. The position or location of a person or “node” or “vertex” in relation to all the others is a primary concern of social network analysis. Many network explanations look for causes of outcomes in the patterns of connections around an individual instead of their personal characteristics. “Know who” is often more important in network explanations than “know how.” Network approaches observe that different people in similar social positions often act in similar ways, even if they have different backgrounds. Positions within networks may be as significant a factor as any aspect of the people who occupy them. Network analysis argues that explanations about the success or failures of organizations are often to be found in the structure of relationships that limit or provide opportunities for interaction [7].

Many network concepts are intuitive and echo familiar phrases like “friend of a friend,” “word of mouth,” and “six degrees of separation.” Other network terms like “transitivity,” “triadic closure,” and “centrality” (see Section 3.5) may be unfamiliar terms for familiar social arrangements. Many of us recognize social network differences among people: we know some people who are “popular” and have connections to many others. We may also know some people who may be less “popular” but are still “influential,” connecting to a smaller number of people who have “better” connections. Network analysis recognizes these and other less intuitively sensed patterns in social relationships, like measuring the number of your friends who know each other and how much a person occupies a gatekeeper or bridge role between two groups. The network analysis approach makes the web of interconnections that bind people to one another visible, creating a mathematical and graphical language that can highlight important people, events, and subgroups.

3.2.1 A simple Twitter network example

To better understand the network perspective, consider the social network of Twitter users shown in Figure 3.1 (see Chapter 11 for a description of Twitter). It is an example of a sociogram, also called a network graph, which is a common way of visualizing networks. Like all networks, it consists of two primary building blocks: vertices (also called nodes or agents) and edges (also called ties or connections). The vertices are represented by images of the Twitter user profile photo, and the edges are represented by the lines that point from one vertex to another.

This network graph visualization paints a picture of the social relationships among the Twitter accounts of members of the United States Senate in 2018. The size of each Twitter user’s profile image is determined by the user’s total number of followers as reported by the Twitter Application Programmer Interface (API), which gives software access to extended details about each user’s profile and message data. This is one example of how attribute data (e.g., data that describe a person) can be overlaid onto a network. A line, or edge, exists between two people when one user account “mentions” or “replies-to” another. All of these connections in aggregate reveal the emergent structure of two large distinct groups (G1 and G2) with relatively few connecting links, which loosely map to the two political parties in the United States. These separate clusters reflect the higher rate that members of one party mention one another in contrast to the rate they mention members of the opposing party. This network analysis identifies the individuals who fill important positions within the network, such as those with whom many other people interact and those who are connected across cluster boundaries. The current and following chapters will provide a guide to creating maps like these from Twitter and other social media platforms and data sources. For now, let’s consider the major components of a network in a bit more detail.

3.2.4 Network data representations

Because network data differ from attribute data, a different way to represent it is used. With attribute data, it is common to create a data matrix where each row represents an individual and each column represents an individual’s characteristics, behaviors, or answers to survey questions. A modified approach is used to represent relational data. Like attribute matrices, each row represents an individual in the network. However, unlike attribute matrices, each column represents other individuals as shown in Table 3.1.

Table 3.1

A network represented as a matrix^a

	Ann	Bob	Carol
Ann	0	1	1
Bob	0	0	0
Carol	1	0	0

^a This network is a directed network, as it is not symmetrical (i.e., Ann points to Bob in row 1, but Bob doesn't point to Ann in row 2). It is a simple binary network: either a tie exists (value = 1) or not (value = 0).

Different types of edges can be represented in network matrices. Table 3.1 describes a directed network because not all connections are reciprocated. For example, Ann “points to” Bob as shown in row 1, but Bob does not “point to” Ann as shown in row 2. If it were an undirected network it would be a symmetric matrix; if Ann points to Bob then Bob must necessarily point to Ann. This network is a binary network because it only includes 1s and 0s, where a 1 indicates that there is a connection and a 0 indicates that there is no connection. Allowing additional values would create a weighted network. For example, the 1s could be replaced with the number of email messages sent or phone calls made to the other person. Notice that the diagonal of the matrix connects each person with himself or herself. In this network, like most networks, the diagonal values are 0 indicating that a person does not “point to” herself. However, in some networks a “self-loop” connecting a person to herself can exist. For example, a person may send herself an email message as a reminder. Network matrices are powerful forms of representation that lend themselves to efficient mathematical manipulation for those inclined. However, they can also become quite large and challenging to navigate, particularly when networks are relatively “sparse” with few connections and many items.

Advanced topic

The foundations of graph theory

Network analysis is rooted in the work of the mathematician Leonhard Euler who in 1736 studied the question whether a single path could be walked over the Seven Bridges of Königsberg that connected islands in the river Pregel (which flows through what was then Prussia and is now Kaliningrad in Russia) without crossing any bridge more than once.¹ By reimagining the problem in terms of vertices and edges, he showed it is impossible to cross each bridge just once. Although the problem seems abstract, its solution led to the development of the mathematics of graph theory and, notably, hundreds of years later, the mathematical work of Paul Erdös and Alfréd Rényi on random graphs in the 1950s, an important theoretical development that allows for the generation of a graph from random processes. Social network analysis builds on these concepts and extends them to capture the nonrandom connections that occur among groups of people.

An alternative to the matrix data format that is a more efficient representation of a network is called an “edge list.” As its name suggests, it is simply a list of all edges in the network as shown in Table 3.2. This is the same network as shown in Table 3.1. Individuals in the Vertex1 column “point to” those in the Vertex2 column. Unless data describing the value of each edge are provided in additional columns, the network is implied to be a binary one. Self-loops are possible to represent in edge lists by having a row with the person’s name repeated in both columns. Throughout this book, you will use edge lists instead of matrices. Edge lists are “efficient” in that they only record a row of data for each connection that does exist in a network, rather than store a “zero” for each possible connection that does not exist. Edge lists can be smaller files and easier to edit and review.

Table 3.2

A network represented as an edge list^a

Vertex1	Vertex2
Ann	Bob
Ann	Carol
Carol	Ann

^a Individuals in the Vertex1 column “point to” those in the Vertex2 column in this directed network. The network is implied to be a binary network. Additional columns could be used to describe each edge. For example, an Edge Weight column could be added with values representing the strength of various ties.

The final method for representing networks is through network graphs. Figure 3.2 is a network graph based on the data in Table 3.2. It makes immediately clear that the relationship between Ann and Carol is reciprocated (i.e., there are arrows on both sides of the line connecting them) and that there is no connection between Bob and Carol. Our earlier analysis of Figure 3.1, another network graph, demonstrates how network graphs can lead to insights that are hard to identify in tabular data, particularly when large networks are presented. However, many network graphs require significant preparation to assure that they are readable as described in Section 3.9 and Chapter 4.

3.3 Types of networks

Social networks range in size from a handful of people to national and planetary populations. They also differ in the types of vertices they include, the nature of the edges that connect them, and the ways in which they are formed. In this section we introduce some of the distinctions that network scientists have identified to describe different types of networks. These distinctions affect the metrics and maps generated from them, as well as their interpretation.

3.4 The network analysis research and practitioner landscape

You can find network scientists in nearly every academic discipline and an increasing number of practitioner communities. Network concepts and techniques are now widely found throughout a range of disciplines including sociology, anthropology, communications, computer science, education, economics, physics, management, information science, medicine, political science, public health, psychology, biology, history and digital humanities. In the past several decades, social scientists have shown that network structures have a profound influence on health, work, and community. Getting a job, being promoted, catching an illness, adopting an innovation, and many more activities and processes have been explained in the terms of social networks. Network structures are important in the biological sciences where research is focused on connections between metabolic and genetic processes. The shape and function of networks can have great consequences as ideas, genes, innovations, or pathogens diffuse through populations. Researchers now apply network theory and methods to understanding how Supreme Court decisions relate to previous cases, how the United States Senate votes (see Chapter 7), how epidemics spread within cities, and how characters in a movie relate to one another (see Chapter 6). Networks are formed from many physical processes and are echoed in a number of structures created inside information systems such as the collection of linked documents within the World Wide Web or an enterprise’s collections of files and emails. Information scientists use these links to identify high-quality web pages (e.g., Google’s PageRank algorithm), or use the citations from research articles to identify high-impact articles and authors.

Network methods are diffusing beyond academic research, becoming an important tool for managing organizations, markets, and movements. Entrepreneurs apply network analysis techniques to understand how to leverage the powerful effects of word-of-mouth marketing as their customers spread news about their new products to one another. Many politicians recognize the potential power of a connected network of supporters who can be turned into contributors, volunteers, and voters. Engineers use network analysis to build more effective power grids, computer networks, and transportation systems. Law enforcement officers and lawyers analyze email networks to identify and prosecute potential criminals. And the intelligence community seeks to identify national security threats by looking at networks created by communication links, money trails and kinship. Having at least a basic understanding of network thinking and concepts is a core literacy of our time. Like statistics, network analysis has countless applications to a number of fields.

This book primarily focuses on social network analysis, a subfield of network sciences that focuses on networks that connect people or social units (i.e., organizations, teams) to one another (see Advanced topic: Early social network analysis). Further, we are interested in networks that connect human-generated content or artifacts together, such as websites or cell phones, or social media networks.

Advanced topic

Early social network analysis

The social science roots of social network analysis can be found in the early 1800s in the work of the person credited with being the first sociologist, Auguste Comte, and later in the early 1900s in the work of the sociologist Georg Simmel. Both saw patterns of social ties as the main focus of sociology in contrast to the study of individuals and their attributes. Early in the 19th century, Comte defined society as more than simply a group of people. He argued that a population became a society only when people had influence on one another and considered the choices and interests of others as part of their own choices. Simmel echoed these ideas at the turn of the twentieth century, focusing social science on the study of how people come together and form groups and associations. These sociologists imagined society as composed of a web of relationships—more than a mass of individuals; they saw societies as networks of interaction and influence.

The idea of connected actions linking people to one another has remained at the core of the social sciences, but efforts to create a systematic language to record social relationships started only in the 20th century. Anthropologists studying the range of kinship systems they documented in fieldwork from around the world created symbol systems that are related to social network analysis. Their maps of who is related to whom were early forms of social networks focused on just the subset of social ties that are considered to be “family.” The core concepts and methods of modern social network analysis date from the 1930s and the pioneering work of Jacob Moreno and his many collaborators. Researchers at New York University, Columbia, and Harvard created the first scholarly works featuring the distinctive core components of modern social network theory: measures, maps, and models. Moreno and his research partners created the first pictures of patterns of groups of people and their partnerships, using visual maps with symbols that represented individuals with different types of lines connecting them to others that represented different kinds of relationships.

Moreno documented relationships among schoolchildren and the way an innovative behavior, running away, moved through chains of student connections. In 1934, Moreno [8] published “Who shall survive,” which catalyzed work among a group of scholars who refined his approach and added critical mathematical elements that today are a standard part of network analysis. These approaches were applied to various settings, and revealed the key roles a relatively small number of people played in their networks along with the presence of subgroups of distinct people. For example, in the 1930s, Davis et al. collected detailed records of observed attendance at 14 social events by 18 southern women, and the graph of that data revealed two distinct groups with minimal overlap [9]. Moreno developed sociometry and is often considered the founder of the sociogram, applying these diagrams in studies of relationships among members of a football team. These diagrams revealed patterns of friendship and animosity (see Figure 3.3) (as produced in Freeman [10]).

At Harvard in the 1930s, a group formed around W. Lloyd Warner and Elton Mayo to explore interpersonal relationship in workplaces. Early social network analysis work focused on connections in small work groups in industrial factory settings. For example, Roethlisberger and Dickson [11] studied the Western Electric Wiring room, documenting the ways individuals within a group worked with one another. As seen in Figure 3.4, some workers in the study emerged as the most connected, whereas others appeared as peripheral or isolated. Another dataset was created that represented the relationships among 14 manufacturing employees of the Western Electric Hawthorne Plant. Employees and two inspectors were observed, and each contact among them was coded. When employees played games with one another, argued, were openly friendly, confrontational, or helpful a note and tie was recorded. The result were six networks, which led to a seminal work by the Harvard sociologist George Homans [12] and later more mathematical work that focused on automatically finding clusters or groups within these datasets [13]. In the 1950s, Nadel wrote about social roles and the social structures that define them [14]. He saw that the patterns of connections people had might be similar, even if they were connected to different people. These patterns, Nadel suggested, could be studied systematically, but in the 1950s the data and computational resources made that ambition a challenge.

Over time, Moreno's colleagues, including Paul Lazarsfeld, added key ingredients of the modern form of social network analysis: metrics and algorithms for calculating important network properties of the graph as a whole and for each individual in the graph (see Freeman [10] for details).

3.5 Network analysis metrics

Social scientists, physicists, computer scientists, and mathematicians have collaborated to create novel theories and algorithms for calculating measurements of social networks and the people and things that populate them. These quantitative network metrics allow analysts to systematically inspect the patterns of connection within the social world, creating a basis on which to compare networks, track changes in a network over time, and determine the relative position of individuals and clusters within a network.

Social network measures initially focused on simple counts of connections and over time became more sophisticated as it developed and incorporated concepts of network density, centrality, structural holes, balance, and transitivity. Some metrics describe a network as a whole. For example, vertex count is the number of entities in the network while the edge count is the number of connections among them. Another whole network metric “density” captures how connected a set of vertices are by calculating the percentage of connections that are observed from maximum possible count if everyone connected to everyone. Other metrics are calculated for each vertex in a network. For example, “centrality” measures, of which there are many, capture how “important” (central) a vertex is within the network based on some objective criteria. Some people sit at the edge or periphery of their networks, whereas others are firmly at the center, connected to many of the other most connected people. In most human networks, even highly connected networks, some pairs of people are not directly connected. When a third person bridges a connection (a “friend of a friend”), we can think of that person as a broker, a “bridge” or a “connector.” When that person is missing, we can think of the gap as a “structural hole,” a place in which there is a missing connector, potentially a good spot to build a “bridge.” The following sections describe some of these metrics in more detail. Chapter 6 introduces some of the core metrics found in NodeXL through hands-on exercises.

3.6 Social networks in the era of abundant computation

The widespread adoption of networked communication technologies has significantly expanded the population of people who are both aware of network concepts and interested in network data. Although the idea of networks of connections of people spanning societies and nations was once esoteric, today many people actively manage an explicit social network of friends, contacts, buddies, associates, and addresses that compose their family, social, professional, and civic lives. Facebook posts forwarded from person to person have become a common and visible example of the ways information passes through networks of connected people. The notion of “friends of friends” is now easy to illustrate in the features of social media applications like Facebook and LinkedIn that provide explicitly named “social networking” services. Viral videos and chain emails illustrate the way word of mouth has moved into computer-mediated communication channels. The idea of “six degrees of separation” has moved from the offices of Harvard sociologists to become the dramatic premise of a Broadway play to now appear as an expected feature of services that allow people to browse and connect to their friend’s friends.

As network concepts have entered everyday life, the previously less visible ties and connections that have always woven people together into relationships, cliques, clusters, groups, teams, partnerships, clans, tribes, coalitions, companies, institutions, organizations, nations, and populations have become more apparent. Patterns of information sharing, investment, personal time and attention have always generated network structures, but only recently have these linkages been made plainly visible to a broad population. In the past few decades, the network approach to thinking about the world has expanded beyond the core population of researchers to a wide range of analysts and practitioners who have applied social network methods and perspectives to understand their businesses, communities, markets, and disciplines. Today, because many of us manage many aspects of our social relationships through a computer-networked social world, it is useful for many more people to develop a language and literacy in the ways networks can be described, analyzed, and visualized. Visualizing and analyzing a social network is an increasingly common personal or business interest. The science of networks is a growing topic of interest and attention, with a growing number of courses for graduates and undergraduates, as well as educational materials for a wider audience (e.g., television documentaries).⁷

The availability of cheaper computing resources and network datasets has enabled a new generation of researchers access to studies of the structures of social relationships at vastly larger scale and detail. Since the late 1960s, as computing resources and network datasets have grown in availability and dropped in cost, researchers began developing tools and concepts that enabled a wider and more sophisticated application of social network analysis.

3.7 The era of abundant social networks: From the desktop to your hand

We now live in a new era of network data abundance. Network data collection was once a time-consuming and laborious process that yielded small datasets at great cost. Observations, surveys and interviews took many days or weeks to perform, could not be repeated frequently, required many people to produce, and often yielded low rates of participation with inherent biases and errors. Asking people about their relationships with others continues to have benefits and offers unique sources of insight, but people have been shown to be a poor source of accurate information as bias and faulty memory warp what people report about who they know and with whom they interact. The challenge of creating a dataset that spanned long periods or large numbers of people or contained records of many events proved insurmountable using traditional methods.

Today, interactions between people increasingly take place through computing systems. Users create many types of networks in a machine-readable form each day as our interactions are documented in a computer. When we use these communication tools, databases are created and maintained with records and log files that document the details of the time, place, and participants of each interaction, whether via computers or telephones or even televisions. These event logs describe many different kinds of connection but share a common structure in which one person or entity is linked to another by some relationship.

The creation of these machine-readable network datasets mean that long periods of time or large populations connected by many events can now be studied using widely available computing equipment and data sources.

Like a jump from Galileo’s handmade telescope to the orbiting Hubble, network science has made a vast leap in scale and scope as we create a digitally networked world around ourselves.

The historical drought of social network data has ended with a flood of new sources of network data. The challenge has shifted to rapidly develop tools and concepts needed to process and analyze this deluge of connected data. Technical methods for building multi-terabyte databases have shifted to the even vaster task of managing petabytes of data. New methods of harnessing thousands and even millions of computers in parallel have been driven by the growing need to manage vast data stores growing from the web. The challenge is likely to grow steeper as new sources of network data come pouring out off an emerging class of sensor-rich devices (the “Internet of Things”) that record vast streams of data from billions of people, devices, and locations. The early wave of this surge of data can be seen in new sources of data from everyday life that are being captured and recorded with mobile and wearable devices, creating a new stream of archival material that is richer than all but the most obsessively observed biographies. It has become common in recent years that the most timely and well-placed photographs and video recordings have come from everyday individuals with phones and computers rather than from news photographers and reporters.

The coming wave of mobile technologies is likely to deepen this trend, with new ways for smartphones or other devices to capture information about their users and the relationships and world around them. Many mobile applications integrate location into their service (see Chapter 2). As phones are aware of their location, a new set of mobile social software applications are becoming possible, as evidenced by new services such as Strava, a good example of a mobile data collection, analysis, and presentation service for cyclists, runners, and other trail sports. Other products like FitBit¹³ and Apple Watch are examples of social location and vital signs recording technologies that enabled web applications to provide self-monitoring medical and fitness tracking. Medical communities overlap with trail-based exercise communities by using devices that extensively quantify your “self” and “others.” These devices enable consumers to collect detailed medical readings nearly all the time that are cross referenced by location. The result is a growing aggregated map of the health and environmental conditions of the planet, not unlike early examples of collectively authored road maps of whole nations accomplished by the Open Street Map project.¹⁴

3.8 Tools for network analysis

The growth of interest in network analysis has been dramatic, but until recently the development of social network analysis tools has lagged, and they remained challenging for many non-technical people to use. Applying network approaches has been traditionally a challenge that involved much more than simply mastering a new set of concepts and ideas that focus on relationships and patterns. Network data have traditionally been difficult to create and collect, and the tools for analyzing and visualizing networks have demanded significant technical skill and often mastery of programming languages. Many tools that exist to support network analysis demand significant commitment to learn and master. The existing network tools that are relatively easier to use have typically lacked support for easily importing social media network data. In the past few years, many network analysis projects and research papers have focused on computer-mediated networks of people, documents, and systems. Only recently have new tools made it simpler for people to extract data from major social media network sources and to perform a basic network analysis workflow without requiring programming skills or using a command line interface.

Social media network data collection, scrubbing, analysis, and display tasks have historically required a remarkable collection of tools and skills. While tools like Datasift make data available from numerous social media platforms, significant technical skills are needed to connect to application programmer interfaces (APIs). In contrast, this book focuses on a single tool designed for non-programmers, NodeXL, because of its relative ease of use, support for rich visuals and analytics, and integration with the ubiquitous Excel spreadsheet software. The python or “R” programming language path is certainly the high road for experts and those with demanding volumes of data or esoteric data analysis requirements. But for the noncoding user, NodeXL may be one of the easiest ways to both manipulate network graphs and get graph datasets from a variety of social media sources. A detailed step-by-step guide to the core features of NodeXL can be found in Part II of the book.

3.9 Node-link diagrams: Visually mapping social networks

One of the key elements that characterizes modern social network analysis is the use of visualizations of complex networks. Compared to staring at edge lists or network matrices (see Section 3.2.4), looking at a network graph can provide an intuitive visual overview of the structure of the network, calling out cliques, clusters, communities, and key participants. It could be said that a graph visualization is worth a thousand ties. Not only can network visualizations inspire understanding and insights, they can also be appealing and even beautiful. They can serve as persuasive tools that demonstrate important points about networks. The ability to map attribute data and network metric scores to visual properties of the vertices and edges (see Chapters 5 and 6) makes them particularly powerful.

However, network visualizations are often as frustrating as they are appealing. Network graphs can rapidly get too dense and large to make out any meaningful patterns as illustrated in Figure 3.5. Many obstacles like vertex occlusions and edge crossings make creating well-organized and readable network graphs challenging. There is an upper limit on the numbers of vertices and edges that can be displayed in a bounded set of pixels; typically only a few hundred or thousand vertices can be meaningfully and distinctly represented on average-sized computer screens. In his appeal for better-quality network visualization, Shneiderman [40] has suggested that we aspire to reach the worthy but not always attainable goal of “netviz nirvana” in which the following goals are proposed:

• • Every vertex is visible.
• • Every vertex’s degree is countable (i.e., the number of connections that start or end at that vertex).
• • Every edge can be followed from source to destination.
• • Clusters and outliers are identifiable.

To approach netviz nirvana, careful preparation, layout, and filtering techniques must be used. In practice, network visualizations often fall far from the mark. However, the graphs shown throughout this book illustrate the value of carefully crafting network graphs. We hope they will inspire network analysts to take the care needed to create substantive, understandable, and esthetically pleasing graphs.

3.10 Common network analysis questions applied to social media

Once a set of social media networks has been constructed and social network measurements have been calculated, the resulting dataset can be used for many applications. For example, network datasets can be used to create reports about community health, comparisons of subgroups, and identification of important individuals, as well as in applications that rank, sort, compare, and search for content and experts.

The value of a social network approach is the ability to ask and answer questions that are not available to other methods. Network methods focus on the patterns of relationships in contrast to the volumes of individuals. Although analysts, marketers, and administrators often track social media participation statistics, they rarely consider measures of network position and structure. Traditional participation statistics can provide important insights into the volume of engagement of a community, but can say little about the structure of the connections between community members. Network analysis can help explain important social phenomena such as group formation, group cohesion, social roles, personal influence, and overall community health. Combining traditional participation metrics with network metrics provides the best of both worlds and allows you to answer important questions such as the following:

• • What kinds of social roles are being performed within a social media collection? Does a community have enough people filling the important roles?
• • Which individuals play important social roles within a group or collection? Who would make a good administrator based on that person’s network position?
• • What subgroups exist? Do connections between subgroups exist? Who plays the bridge roles that connect otherwise unconnected groups?
• • How do new ideas propagate through a network? Who are the influencers that spark the spread of ideas?
• • How do the overall structures of a social network change after a particular event (e.g., a company social, a round of new hires or layoffs, a product launch or recall)?

3.11 Practitioner’s summary

The opportunities for practitioners to apply network analysis to contemporary business, community management, political influence, and team collaboration have dramatically increased in recent years. The once esoteric concepts and metrics of network analysis have become talk show and airport lounge topics. The difficulties in collecting and analyzing network data have been dramatically reduced by powerful database methods and well-designed network analysis and visualization tools. There is still a lot of work to be done, but practitioners now have the potential to make more effective decisions based on network analyses of their own data conducted in a few hours, rather than a few months.

Learning network concepts and tools is a necessary first step, but the payoffs for applying network methods are large. The growing numbers of trained social media network analysts and consultants are complemented by a vast array of books and informative websites, online seminars, and Wikipedia pages which make the necessary training widely available. At the same time, network analysis methods are rapidly spreading through university curricula and filtering into high school courses.

Attending public seminars and professional conferences provides other means to acquire skills and make valuable connections. Your first steps may be a struggle, but we hope that with each step the processes become smoother and the professional benefits larger.

3.12 Researcher’s agenda

The research progress on network analysis has been dramatic in the past few decades, transforming an exotic research topic into a thriving research community in academia, government, and industry. The existing metrics, clustering, and layout algorithms are stabilizing, but innovative approaches are still emerging to trigger bursts of new research. As practitioner pressure builds to apply network analysis to ever larger datasets, researchers have developed remarkably more efficient algorithms, while hardware developers have produced powerful graphics processors (based on gaming computers), huge arrays of computers, and scalable cloud computing services. Meanwhile, new social media services generate more relational data than ever before, ushering in a golden era of social science research on human relationships and collaboration.

The algorithms and hardware provide the platforms, but the concomitant development of vastly improved user interfaces for network analysis has begun to enlarge the community of users from the dedicated sociologists who are also programmers to the broad segment of business analysts who use spreadsheets or simplified web-based tools. Packaging the complex processes of frequently applied network analyses into a few clicks is the next challenge in many fields, thereby inspiring other researchers and developers to simplify the processes even further, while increasing the power offered to users. The best is yet to come.