Chapter 11 Further Reading

Foundational Network Science

Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. The original paper introducing the Barabási-Albert model of preferential attachment. Mathematically dense but the introduction and conclusion are accessible. Essential for understanding the formal basis of scale-free network formation.

Barabási, A.-L. (2016). Network Science. Cambridge University Press. A comprehensive, freely available textbook on network science (available at networksciencebook.com). Chapters 4–6 (scale-free networks, the Barabási-Albert model, evolving networks) are most relevant to Chapter 11's content. Clear exposition, excellent visualizations.

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684), 440–442. Introduces the small-world property: high clustering coefficient combined with short average path length. Chapter 11 uses this concept to explain why information spreads quickly in fan communities despite low overall network density.

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. The foundational paper on weak tie theory. Granovetter's original application was to job-seeking behavior, but his theoretical argument about information diversity and weak ties translates directly to fan community dynamics.

Burt, R. S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press. The theoretical foundation for Section 11.4. Burt's structural holes theory explains how brokerage positions (like Priya Anand's cross-community position) generate information and influence advantages.

Social Network Analysis Methods

Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. The standard reference for social network analysis methodology. Dense but comprehensive; Chapter 3 (notation and basic definitions) and Chapter 5 (centrality) are most relevant.

Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press. Excellent introduction to network science from a physicist's perspective. Chapter 14 (community structure) is directly relevant to Section 11.6.

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, P10008. The paper introducing the Louvain community detection algorithm used in this chapter's community_detection.py. Explains the algorithm's rationale and performance characteristics.

Fan Community Network Research

Fiesler, C., & Dym, B. (2020). Moving targets: An analysis of community rules on and across digital platforms. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1), 1–25. Empirical research on fan community governance across platforms, including analysis of migration patterns after platform changes. Directly relevant to Sections 11.7 and Case Study 11.1.

Dym, B., Fiesler, C., & Bruckman, A. (2018). Vulnerable and online: Fanfiction and the benefits of pseudonymity. Proceedings of CSCW 2018. Examines the structure of online fan fiction communities and the role of pseudonymity in enabling participation. Provides empirical context for the AO3 community network.

Evans, E. (2020). Fandom online communities: Network analysis of AO3 author-reader relationships. Transformative Works and Cultures, 32. Empirical network analysis of AO3 creative production and reception. Documents the degree distribution inequality in fan fiction readership that confirms scale-free properties in creative fan networks.

Woo, B. (2018). Getting greasy: The crisis of legitimation in geek culture. Journal of Fandom Studies, 6(2), 123–139. While primarily about legitimation (relevant to Chapter 12), includes material on the network structure of geek/fan communities and how insider/outsider positions map onto structural positions.

Platform Ecology and Network Resilience

Gillespie, T. (2018). Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media. Yale University Press. Examines how platform decisions shape community network structure. Essential background for Section 11.7's discussion of platform governance and network vulnerability.

Bucher, T. (2018). If... Then: Algorithmic Power and Politics. Oxford University Press. Analyzes how algorithmic recommendation and promotion systems shape what content and which nodes become visible — directly affecting the preferential attachment dynamics that produce hubs.

Matias, J. N. (2019). Preventing harassment and increasing group cohesion in online communities with low-cost interventions. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW). Empirical study of moderation interventions and their effects on network structure and community cohesion. Connects Chapter 11's network analysis to Chapter 13's governance themes.

ARMY and K-Pop Fan Networks

Jin, D. Y. (2021). Globalization and Media in the Digital Platform Age. Routledge. Includes analysis of K-pop fan networks and their global coordination mechanisms. Provides academic context for the ARMY Files case studies.

Kim, Y.-A. (2019). Crowdsourced fandom: K-Pop global fan practices and online labor. Media, Culture & Society, 41(8), 1168–1184. Examines how K-pop fan coordination practices — including streaming coordination — work as global distributed network phenomena. Directly relevant to the ARMY Files case studies in Chapters 11 and beyond.

Python Resources for Network Analysis

NetworkX documentation: https://networkx.org/documentation/ The official documentation for the NetworkX library used in this chapter's code. Comprehensive reference for all graph algorithms and network analysis functions used in fan_network_analysis.py and community_detection.py.

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science and Engineering, 9(3), 90–95. The original paper for the matplotlib visualization library. The documentation at matplotlib.org is the primary practical reference.

Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. While not used in Chapter 11's code directly, scikit-learn provides additional network analysis and machine learning tools that extend the analyses here.