So we've seen in the course of this book the many wondrous things that can be done using Gephi, and we've still really just scratched the surface. There are now so many opportunities to process, analyze, and visualize network datasets that it can be overwhelming at times. Countless examples of Twitter, Facebook, and other social networks proliferate across the Web, some of them thoughtful and powerful, some others far less so. Likewise, we have all seen many instances of collaboration networks, protein networks, cell structure networks, citation networks, and so on. Where does it all go from here? Will the future look like the present, simply with more examples?

One outcome that seems a near certainty will be the increasing involvement of end users in interacting and perhaps even participating in the creation of the eventual network display. Imagine a case where users can adjust and adapt to the graph on a real-time basis, affecting the outcome of the network. Look no further than many of the massive multiplayer online games for proof of what can be done at the user level. Perhaps game theory examples will involve multiple end users acting according to their own preferences of the moment, with the network adapting and evolving to reflect the instantaneous input of hundreds or even thousands of users. These real-time examples could change much of today's analysis from a dependence on static historical datasets to an environment that is plugged into the ongoing changes in an evolving network.

Imagine also the possibilities of delivering additional insight by providing relevant information behind every node and edge in a network. In today's world, we have the ability to craft templates and provide generally static information to accompany our network graphs. In some cases, this is abetted by external links that provide additional information. This approach, while useful, puts the burden on the user and provides something less than a seamless process. How can we improve this process?

In the near term, are there untapped opportunities where network analysis can work more closely with ontological resources to deliver richer information and interactivity to the end user? In the slightly more distant future could all relevant information be accessed within the bounds of a single network page, enabling users to travel through the network as if they were in a gaming environment? Consider the possibilities of adding relevant information that activates multiple senses, and how users could leverage the power of sight, sound, and touch to examine and understand the network. Users could perhaps access a sort of virtual world, but one that is based totally on the reality of moment to moment interactions, not on some fictional construct created by game designers.

Beyond the evolution of network graph analysis through technological advances there is the question of additional use cases. We have seen the many instances of social media, citation, collaboration, biological, and infrastructure networks, but are there other opportunities to leverage network analysis to understand systems? Could network graph analysis be a powerful tool for exploring topics such as chaos theory, tracking the impact of specific stimuli on a surrounding network? Might we also be able to explore connections within the body that could help combat disease or infection? Are there greater opportunities to use these tools to better predict long-term effects of specific short-term decisions, in a modeling sort of environment?

These are merely a few examples for where network graph analysis could be heading. You no doubt have additional thoughts about the future and where the opportunities will emerge. Regardless of our individual ideas, I believe we can all agree that the future will be filled with exciting opportunities to employ these methods to better understand our world and all of its interactions. The future of network graph analysis and all its manifestations promises to be both challenging and rewarding, and will evolve and grow through the efforts of readers like yourself.

I also invite you to view my personal explorations and discussions at http://visual-baseball.com/wordpress/?gallery=network-graphs as well as the great work of users in the Gephi communities on Facebook and LinkedIn. Some truly exceptional work is being shared in these forums.