SOCIOL 208A Reading Schedule (Fall 2024)

1 Week 1, April 1: Basic SNA Concepts

1.1 Readings

Prell, C. & Schaefer, D. R. (2023). Introducing Social Network Analysis. In J. McLevey, J. Scott, P. J. Carrington (Eds.) The SAGE Handbook of Social Network Analysis. Sage Publications. link
Light, R. & Moody, J. (2021). Network Basics: Points, Lines, and Positions. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks Oxford University Press. link
Harary, F. & Norman, R. Z. (1953). Graph Theory as a Mathematical Model in Social Science. Research Center for Group Dynamics, University of Michigan. link

1.2 Other Material

Basic Network Concepts and Definitions Cheat Sheet.
Basic Introduction to R
The Basics of the R Programming Language
Short Intergraph Tutorial
Package networkdata

2 Week 2, April 8: Centrality

2.1 Readings

Borgatti, S. P., & Everett, M. G. (2006). A graph-theoretic perspective on centrality. Social Networks, 28(4), 466-484. link
Martin G. Everett & Steve P. Borgatti (2023). “Centrality.” In J. McLevey, J. Scott, P. J. Carrington (Eds.) The SAGE Handbook of Social Network Analysis. Sage Publications. link
Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. Social Networks, 1(3), 215-239. pdf
Agneessens, F., Borgatti, S. P., & Everett, M. G. (2017). Geodesic-based centrality: Unifying the local and the global. Social Networks, 49, 12-26. link

2.2 Other Material & Further Reading

Brandes, U., Borgatti, S. P., & Freeman, L. C. (2016). Maintaining the duality of closeness and betweenness centrality. Social networks, 44, 153-159. link
Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O. (2005). Centrality Indices. In: Brandes, U., Erlebach, T. (eds) Network Analysis. Lecture Notes in Computer Science, vol 3418. Springer, Berlin, Heidelberg (secs. 3.2, 3.3, and 3.4). link
Koschützki, D., Lehmann, K.A., Tenfelde-Podehl, D., Zlotowski, O. (2005). Advanced Centrality Concepts. In: Brandes, U., Erlebach, T. (eds) Network Analysis. Lecture Notes in Computer Science, vol 3418. Springer, Berlin, Heidelberg. link
Comprehensive list of centrality measures with formulas and software

3 Week 3, April 15: Ego Networks

3.1 Readings

Smith, J. A. (2021). The Continued Relevance of Ego Network Data. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks Oxford University Press. link

4 Week 4, April 22: Status and Prestige

Franceschet, M. (2011). PageRank: standing on the shoulders of giants. Communications of the ACM, 54(6), 92-101. link
Gleich, D. F. (2015). PageRank beyond the web. SIAM Review, 57(3), 321-363. link
Martin, J. L. & Murphy, J. P. (2021). Networks, Status, and Inequality. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks Oxford University Press. link
Rossman, G., Esparza, N., & Bonacich, P. (2010). I’d Like To Thank The Academy, Team Spillovers, and Network Centrality. American Sociological Review, 75(1), 31-51. link

4.1 Further (Mathy) Reading

Vigna, S. (2016). Spectral ranking. Network Science, 4(4), 433-445. pdf
Baltz, A., Kliemann, L. (2005). Spectral Analysis. In: Brandes, U., Erlebach, T. (eds) Network Analysis. Lecture Notes in Computer Science, vol 3418. Springer, Berlin, Heidelberg. link
Bonacich, P. (1972). Factoring and Weighting Approaches to Status Scores and Clique Identification. Journal of Mathematical Sociology, 2(1), 113-120. pdf
Katz, L. (1953). A New Status Index Derived from Sociometric Analysis. Psychometrika, 18(1), 39-43. pdf

5 Week 5, April 29: Similarity, Roles, and Positions

5.1 Readings

Burt, R. S. (1976). Positions in networks. Social Forces, 55(1), 93-122. link
Breiger, R. L., Boorman, S. A., & Arabie, P. (1975). An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. Journal of Mathematical Psychology, 12(3), 328-383. link
Lü, L., Jin, C. H., & Zhou, T. (2009). Similarity index based on local paths for link prediction of complex networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 80(4), 046122. link
Jeh, G., & Widom, J. (2002). Simrank: a measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 538-543). link
Leicht, E. A., Holme, P., & Newman, M. E. (2006). Vertex similarity in networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 73(2), 026120. link

5.2 Further Reading

Fouss, F., Pirotte, A., Renders, J. M., & Saerens, M. (2007). Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on knowledge and data engineering, 19(3), 355-369. link
Kovács, B. (2010). A generalized model of relational similarity. Social Networks, 32(3), 197-211. link
Liben-Nowell, D., & Kleinberg, J. (2003). The link prediction problem for social networks. In Proceedings of the Twelfth Annual ACM International Conference on Information and Knowledge Management (CIKM’03) (pp. 556-559). link to longer paper
Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications, 390(6), 1150-1170. link

5.3 Cheat Sheets:

Centrality, Status, and Node Similarity Cheat Sheet.
Chroł, B & Bojanowski, M. (2018). Proximity-based Methods for Link Prediction. https://cran.r-project.org/web/packages/linkprediction/vignettes/proxfun.html

6 Week 6, May 6: Community Detection

6.1 Readings

Shai, S., Stanley, N., Granell, C., Taylor, D. & Mucha, P. J. (2021). Case Studies in Network Community Detection. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks Oxford University Press. link
Newman, M. E. (2018). Community Structure. In Networks, 2nd Edition. Oxford, Online Edition, Oxford Academic. link
Melamed, D. (2015). Communities of classes: A network approach to social mobility. Research in Social Stratification and Mobility, 41, 56-65. link

6.2 Further (Substantive) Readings

Moody, J., & Mucha, P. J. (2023). Structural Cohesion and Cohesive Groups. In J. McLevey, J. Scott, P. J. Carrington (Eds.) The SAGE Handbook of Social Network Analysis. Sage Publications. link
Shwed, U., & Bearman, P. S. (2010). The temporal structure of scientific consensus formation. American Sociological Review, 75(6), 817-840. link

6.3 Further (Mathy) Reading

Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 70(6), 066111. link
Fortunato, S. (2010). Community Detection in Graphs. Physics Reports, 486(3-5), 75-174. link
Girvan, M., & Newman, M. E. (2002). Community Structure in Social and Biological Networks. Proceedings of the National academy of Sciences, 99(12), 7821-7826. link
Leicht, E. A., and Newman, M. E. (2008). Community Structure in Directed Networks. Physical Review Letters 100, 118703. link
Newman, M. E. (2006). Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences, 103(23), 8577-8582. link
Newman, M. E., & Girvan, M. (2003). Mixing patterns and community structure in networks. In Statistical mechanics of complex networks (pp. 66-87). Berlin, Heidelberg: Springer Berlin Heidelberg.
Newman, M. E. (2003). Mixing Patterns in Networks. Physical review E 67(2), 026126. link
Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. link

7 Week 7, May 13: Analyzing Two-Mode Networks

7.1 Readings

Breiger, R. L. (1974). The Duality of Persons and Groups. Social Forces, 53(2), 181–190. link
Borgatti, S. P., & Everett, M. G. (1997). Network Analysis of 2-Mode Data. Social Networks, 19(3), 243-269. pdf
Everett, M. G., & Borgatti, S. P. (2013). The Dual-Projection Approach for Two-Mode Networks. Social Networks, 35(2), 204-210. link
Neal, Z. (2014). The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance, and other co-behaviors. Social Networks, 39, 84-97. link

8 Further Reading

Borgatti, S., & Halgin, D. (2014). Analyzing affiliation networks. In The SAGE Handbook of Social Network Analysis, First Edition (pp. 417-433), SAGE Publications Ltd. link
Faust, K. (1997). Centrality in affiliation networks. Social Networks, 19(2), 157-191. link

8.1 Other Material

Murphy, Phil, and Brendan Knapp. (2018). Bipartite/two-mode networks in igraph. RPubs https://rpubs.com/pjmurphy/317838
Domagalski, R., Neal, Z. P., & Sagan, B. (2021). Backbone: An R package for extracting the backbone of bipartite projections. Plos one, 16(1), e0244363. link
Neal, Z. P. (2022). backbone: An R package to extract network backbones. PloS one, 17(5), e0269137. link

9 Week 8, May 20: No Class (Traveling)

10 Week 9, May 27: Statistical Models of Network Structure

10.1 Readings

Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191. link
Morris, M., Handcock, M. S., & Hunter, D. R. (2008). Specification of exponential-family random graph models: terms and computational aspects. Journal of Statistical Software, 24(4), 1548. link
Pattison, P., & Robins, G. (2002). Neighborhood–based models for social networks. Sociological Methodology, 32(1), 301-337. link

11 Further Reading

Lusher D., Wang, P., Brennecke, J., Brailly J., Faye, M., Gallagher, C. (2021). Advances in Exponential Random Graph Models. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks, Oxford University Press. link
Orsini, C., Dankulov, M. M., Colomer-de-Simón, P., Jamakovic, A., Mahadevan, P., Vahdat, A., … & Krioukov, D. (2015). Quantifying randomness in real networks. Nature communications, 6(1), 8627. link

12 Week 10, June 3: No Class (Traveling)

--- title: "SOCIOL 208A Reading Schedule (Fall 2024)" --- ## Week 1, April 1: Basic SNA Concepts ### Readings - Prell, C. & Schaefer, D. R. (2023). Introducing Social Network Analysis. In J. McLevey, J. Scott, P. J. Carrington (Eds.) *The SAGE Handbook of Social Network Analysis*. Sage Publications. [link](https://methods.sagepub.com/book/the-sage-handbook-of-social-network-analysis-2e-srm/i388.xml) - Light, R. & Moody, J. (2021). Network Basics: Points, Lines, and Positions. In R. Light and J. Moody (Eds.) *The Oxford Handbook of Social Networks* Oxford University Press. [link](https://doi.org/10.1093/oxfordhb/9780190251765.013.2) - Harary, F. & Norman, R. Z. (1953). Graph Theory as a Mathematical Model in Social Science. Research Center for Group Dynamics, University of Michigan. [link](https://www.idiosophy.com/wp-content/uploads/2017/07/harary-norman.pdf) ### Other Material - Basic Network Concepts and Definitions [Cheat Sheet](https://cazabetremy.fr/Teaching/CN/Definitions.pdf). - [Basic Introduction to R](https://inarwhal.github.io/NetworkAnalysisR-book/ch2-intro-R.html) - [The Basics of the R Programming Language](https://ona-book.org/the-basics-of-the-r-programming-language.html) - [Short Intergraph Tutorial](https://cran.r-project.org/web/packages/intergraph/vignettes/howto.html) - [Package networkdata](https://schochastics.github.io/networkdata/) ## Week 2, April 8: Centrality ### Readings - Borgatti, S. P., & Everett, M. G. (2006). A graph-theoretic perspective on centrality. *Social Networks*, 28(4), 466-484. [link](https://doi.org/10.1016/j.socnet.2005.11.005) - Martin G. Everett & Steve P. Borgatti (2023). "Centrality." In J. McLevey, J. Scott, P. J. Carrington (Eds.) *The SAGE Handbook of Social Network Analysis*. Sage Publications. [link](https://methods.sagepub.com/book/the-sage-handbook-of-social-network-analysis-2e-srm/i4087.xml) - Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. *Social Networks*, 1(3), 215-239. [pdf](https://www.albany.edu/~ravi/pdfs/freeman_1978.pdf) - Agneessens, F., Borgatti, S. P., & Everett, M. G. (2017). Geodesic-based centrality: Unifying the local and the global. *Social Networks*, 49, 12-26. [link](https://doi.org/10.1016/j.socnet.2016.09.005) ### Other Material & Further Reading - Brandes, U., Borgatti, S. P., & Freeman, L. C. (2016). Maintaining the duality of closeness and betweenness centrality. Social networks, 44, 153-159. [link](https://doi.org/10.1016/j.socnet.2015.08.003) - Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O. (2005). Centrality Indices. In: Brandes, U., Erlebach, T. (eds) *Network Analysis. Lecture Notes in Computer Science*, vol 3418. Springer, Berlin, Heidelberg (secs. 3.2, 3.3, and 3.4). [link](https://doi.org/10.1007/978-3-540-31955-9_3) - Koschützki, D., Lehmann, K.A., Tenfelde-Podehl, D., Zlotowski, O. (2005). Advanced Centrality Concepts. In: Brandes, U., Erlebach, T. (eds) *Network Analysis. Lecture Notes in Computer Science*, vol 3418. Springer, Berlin, Heidelberg. [link](https://doi.org/10.1007/978-3-540-31955-9_5) - [Comprehensive list of centrality measures with formulas and software](https://www.centiserver.ir/) ## Week 3, April 15: Ego Networks ### Readings - Smith, J. A. (2021). The Continued Relevance of Ego Network Data. In R. Light and J. Moody (Eds.) *The Oxford Handbook of Social Networks* Oxford University Press. [link](https://doi.org/10.1093/oxfordhb/9780190251765.013.15) ## Week 4, April 22: Status and Prestige - Franceschet, M. (2011). PageRank: standing on the shoulders of giants. *Communications of the ACM*, 54(6), 92-101. [link](https://doi.org/10.1145/1953122.1953146) - Gleich, D. F. (2015). PageRank beyond the web. *SIAM Review*, 57(3), 321-363. [link](https://doi.org/10.1137/140976649) - Martin, J. L. & Murphy, J. P. (2021). Networks, Status, and Inequality. In R. Light and J. Moody (Eds.) *The Oxford Handbook of Social Networks* Oxford University Press. [link](https://doi.org/10.1093/oxfordhb/9780190251765.013.4) - Rossman, G., Esparza, N., & Bonacich, P. (2010). I’d Like To Thank The Academy, Team Spillovers, and Network Centrality. *American Sociological Review*, 75(1), 31-51. [link](https://doi.org/10.1177/0003122409359164) ### Further (Mathy) Reading - Vigna, S. (2016). Spectral ranking. Network Science, 4(4), 433-445. [pdf](https://doi.org/10.1017/nws.2016.21) - Baltz, A., Kliemann, L. (2005). Spectral Analysis. In: Brandes, U., Erlebach, T. (eds) *Network Analysis. Lecture Notes in Computer Science*, vol 3418. Springer, Berlin, Heidelberg. [link](https://doi.org/10.1007/978-3-540-31955-9_14) - Bonacich, P. (1972). Factoring and Weighting Approaches to Status Scores and Clique Identification. *Journal of Mathematical Sociology*, 2(1), 113-120. [pdf](https://www.tandfonline.com/doi/pdf/10.1080/0022250X.1972.9989806) - Katz, L. (1953). A New Status Index Derived from Sociometric Analysis. *Psychometrika*, 18(1), 39-43. [pdf](https://link.springer.com/content/pdf/10.1007/BF02289026.pdf) ## Week 5, April 29: Similarity, Roles, and Positions ### Readings - Burt, R. S. (1976). Positions in networks. *Social Forces*, 55(1), 93-122. [link](https://doi.org/10.1093/sf/55.1.93) - Breiger, R. L., Boorman, S. A., & Arabie, P. (1975). An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. *Journal of Mathematical Psychology*, 12(3), 328-383. [link](https://doi.org/10.1016/0022-2496(75)90028-0) - Lü, L., Jin, C. H., & Zhou, T. (2009). Similarity index based on local paths for link prediction of complex networks. *Physical Review E—Statistical, Nonlinear, and Soft Matter Physics*, 80(4), 046122. [link](https://link.aps.org/doi/10.1103/PhysRevE.80.046122) - Jeh, G., & Widom, J. (2002). Simrank: a measure of structural-context similarity. In *Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining* (pp. 538-543). [link](https://doi.org/10.1145/775047.775126) - Leicht, E. A., Holme, P., & Newman, M. E. (2006). Vertex similarity in networks. *Physical Review E—Statistical, Nonlinear, and Soft Matter Physics*, 73(2), 026120. [link](https://doi.org/10.1103/PhysRevE.73.026120) ### Further Reading - Fouss, F., Pirotte, A., Renders, J. M., & Saerens, M. (2007). Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on knowledge and data engineering, 19(3), 355-369. [link](https://ieeexplore.ieee.org/document/4072747) - Kovács, B. (2010). A generalized model of relational similarity. *Social Networks*, 32(3), 197-211. [link](https://doi.org/10.1016/j.socnet.2010.02.001) - Liben-Nowell, D., & Kleinberg, J. (2003). The link prediction problem for social networks. In *Proceedings of the Twelfth Annual ACM International Conference on Information and Knowledge Management (CIKM’03)* (pp. 556-559). [link to longer paper](https://www.khoury.northeastern.edu/home/cbw/static/class/5750/papers/link-pred.pdf) - Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications, 390(6), 1150-1170. [link](https://doi.org/10.1016/j.physa.2010.11.027) ### Cheat Sheets: - Centrality, Status, and Node Similarity [Cheat Sheet](https://cazabetremy.fr/Teaching/CN/Centralities.pdf). - Chroł, B & Bojanowski, M. (2018). Proximity-based Methods for Link Prediction. [https://cran.r-project.org/web/packages/linkprediction/vignettes/proxfun.html](https://cran.r-project.org/web/packages/linkprediction/vignettes/proxfun.html) ## Week 6, May 6: Community Detection ### Readings - Shai, S., Stanley, N., Granell, C., Taylor, D. & Mucha, P. J. (2021). Case Studies in Network Community Detection. In R. Light and J. Moody (Eds.) *The Oxford Handbook of Social Networks* Oxford University Press. [link](https://doi.org/10.1093/oxfordhb/9780190251765.013.16) - Newman, M. E. (2018). Community Structure. In *Networks, 2nd Edition*. Oxford, Online Edition, Oxford Academic. [link](https://doi.org/10.1093/oso/9780198805090.003.0014) - Melamed, D. (2015). Communities of classes: A network approach to social mobility. *Research in Social Stratification and Mobility*, 41, 56-65. [link](https://doi.org/10.1016/j.rssm.2015.05.001) ### Further (Substantive) Readings - Moody, J., & Mucha, P. J. (2023). Structural Cohesion and Cohesive Groups. In J. McLevey, J. Scott, P. J. Carrington (Eds.) *The SAGE Handbook of Social Network Analysis*. Sage Publications. [link](https://methods.sagepub.com/book/the-sage-handbook-of-social-network-analysis-2e-srm/i4183.xml) - Shwed, U., & Bearman, P. S. (2010). The temporal structure of scientific consensus formation. *American Sociological Review*, 75(6), 817-840. [link](https://doi.org/10.1177/0003122410388488) ### Further (Mathy) Reading - Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. *Physical Review E—Statistical, Nonlinear, and Soft Matter Physics*, 70(6), 066111. [link](https://doi.org/10.1103/PhysRevE.70.066111) - Fortunato, S. (2010). Community Detection in Graphs. *Physics Reports*, 486(3-5), 75-174. [link](https://doi.org/10.1016/j.physrep.2009.11.002) - Girvan, M., & Newman, M. E. (2002). Community Structure in Social and Biological Networks. *Proceedings of the National academy of Sciences*, 99(12), 7821-7826. [link](https://doi.org/10.1073/pnas.122653799) - Leicht, E. A., and Newman, M. E. (2008). Community Structure in Directed Networks. Physical Review Letters 100, 118703. [link](https://doi.org/10.1103/PhysRevLett.100.118703) - Newman, M. E. (2006). Modularity and Community Structure in Networks. *Proceedings of the National Academy of Sciences*, 103(23), 8577-8582. [link](https://www.pnas.org/doi/full/10.1073/pnas.0601602103) - Newman, M. E., & Girvan, M. (2003). Mixing patterns and community structure in networks. In *Statistical mechanics of complex networks* (pp. 66-87). Berlin, Heidelberg: Springer Berlin Heidelberg. - Newman, M. E. (2003). Mixing Patterns in Networks. *Physical review E* 67(2), 026126. [link](https://doi.org/10.1103/PhysRevE.67.026126) - Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. [link](https://doi.org/10.1103/PhysRevE.69.026113) ## Week 7, May 13: Analyzing Two-Mode Networks ### Readings - Breiger, R. L. (1974). The Duality of Persons and Groups. *Social Forces*, 53(2), 181–190. [link](https://www.jstor.org/stable/2576011) - Borgatti, S. P., & Everett, M. G. (1997). Network Analysis of 2-Mode Data. *Social Networks*, 19(3), 243-269. [pdf](http://www.casos.cs.cmu.edu/events/summer_institute/2006/reading_list/borgatti/Borgatti_Network_Analysis.pdf) - Everett, M. G., & Borgatti, S. P. (2013). The Dual-Projection Approach for Two-Mode Networks. *Social Networks*, 35(2), 204-210. [link](https://doi.org/10.1016/j.socnet.2012.05.004) - Neal, Z. (2014). The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance, and other co-behaviors. *Social Networks*, 39, 84-97. [link](https://doi.org/10.1016/j.socnet.2014.06.001) ## Further Reading - Borgatti, S., & Halgin, D. (2014). Analyzing affiliation networks. In *The SAGE Handbook of Social Network Analysis, First Edition* (pp. 417-433), SAGE Publications Ltd. [link](https://methods.sagepub.com/book/the-sage-handbook-of-social-network-analysis/n28.xml) - Faust, K. (1997). Centrality in affiliation networks. *Social Networks*, 19(2), 157-191. [link](https://doi.org/10.1016/S0378-8733(96)00300-0) ### Other Material - Murphy, Phil, and Brendan Knapp. (2018). Bipartite/two-mode networks in igraph. *RPubs* [https://rpubs.com/pjmurphy/317838](https://rpubs.com/pjmurphy/317838) - Domagalski, R., Neal, Z. P., & Sagan, B. (2021). Backbone: An R package for extracting the backbone of bipartite projections. Plos one, 16(1), e0244363. [link](https://doi.org/10.1371/journal.pone.0244363) - Neal, Z. P. (2022). backbone: An R package to extract network backbones. *PloS one*, 17(5), e0269137. [link](https://doi.org/10.1371/journal.pone.0269137) ## Week 8, May 20: No Class (Traveling) ## Week 9, May 27: Statistical Models of Network Structure ### Readings - Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p\*) models for social networks. *Social Networks*, 29(2), 173-191. [link](https://doi.org/10.1016/j.socnet.2006.08.002) - Morris, M., Handcock, M. S., & Hunter, D. R. (2008). Specification of exponential-family random graph models: terms and computational aspects. *Journal of Statistical Software*, 24(4), 1548. [link](https://www.inf.uni-konstanz.de/exalgo/lehre/ws14/nm/local/papers/specifying_ergms.pdf) - Pattison, P., & Robins, G. (2002). Neighborhood–based models for social networks. *Sociological Methodology*, 32(1), 301-337. [link](https://doi.org/10.1111/1467-9531.00119) ## Further Reading - Lusher D., Wang, P., Brennecke, J., Brailly J., Faye, M., Gallagher, C. (2021). Advances in Exponential Random Graph Models. In R. Light and J. Moody (Eds.) *The Oxford Handbook of Social Networks*, Oxford University Press. [link](https://doi.org/10.1093/oxfordhb/9780190251765.013.18) - Orsini, C., Dankulov, M. M., Colomer-de-Simón, P., Jamakovic, A., Mahadevan, P., Vahdat, A., ... & Krioukov, D. (2015). Quantifying randomness in real networks. *Nature communications*, 6(1), 8627. [link](https://doi.org/10.1038/ncomms9627) ## Week 10, June 3: No Class (Traveling)