Training in the Network Science PhD program follows a research mentorship model, combining coursework with close research collaborations between students and faculty.
From the very beginning of your doctoral studies, you will be deeply engaged in research activities, dedicated to respectful, meaningful discourse and rigorous collaborative exchange that embodies the spirit of the Network Science Institute. You’ll work closely with a research group on intensive projects—in areas such as epidemiology (disease networks), political science (mis/information networks), brain science (neural networks), group performance (social networks), and urban planning (infrastructure networks)—in preparation to independently develop research questions and conduct your own analyses.
Prospective students are encouraged to contact faculty and inquire about specific projects and research interests. New projects are always in development.
Training in the Network Science PhD program follows a research mentorship model, combining coursework with close research collaborations between students and faculty.
From the very beginning of your doctoral studies, you will be deeply engaged in research activities, dedicated to respectful, meaningful discourse and rigorous collaborative exchange that embodies the spirit of the Network Science Institute. You’ll work closely with a research group on intensive projects—in areas such as epidemiology (disease networks), political science (mis/information networks), brain science (neural networks), group performance (social networks), and urban planning (infrastructure networks)—in preparation to independently develop research questions and conduct your own analyses.
Prospective students are encouraged to contact faculty and inquire about specific projects and research interests. New projects are always in development.
Training in the Network Science PhD program follows a research mentorship model, combining coursework with close research collaborations between students and faculty.
From the very beginning of your doctoral studies, you will be deeply engaged in research activities, dedicated to respectful, meaningful discourse and rigorous collaborative exchange that embodies the spirit of the Network Science Institute. You’ll work closely with a research group on intensive projects—in areas such as epidemiology (disease networks), political science (mis/information networks), brain science (neural networks), group performance (social networks), and urban planning (infrastructure networks)—in preparation to independently develop research questions and conduct your own analyses.
Prospective students are encouraged to contact faculty and inquire about specific projects and research interests. New projects are always in development.
Training in the Network Science PhD program follows a research mentorship model, combining coursework with close research collaborations between students and faculty.
From the very beginning of your doctoral studies, you will be deeply engaged in research activities, dedicated to respectful, meaningful discourse and rigorous collaborative exchange that embodies the spirit of the Network Science Institute. You’ll work closely with a research group on intensive projects—in areas such as epidemiology (disease networks), political science (mis/information networks), brain science (neural networks), group performance (social networks), and urban planning (infrastructure networks)—in preparation to independently develop research questions and conduct your own analyses.
Prospective students are encouraged to contact faculty and inquire about specific projects and research interests. New projects are always in development.
Network science is an interdisciplinary and thriving area requiring a wide range of knowledge from various fields. Because students come from heterogeneous backgrounds, there is a lot of variation in incoming knowledge, particularly with the many disciplines they will need to master.
In Fall 2017 and 2018, the Network Science Graduate Student Association (NetSI GSA) hosted a bootcamp to kick off the first term. This bootcamp served as an academic orientation, providing a broad overview of topics students need to know. The primary goal is to reduce incoming students’ “unknown unknowns” while providing the tools and resources students need to succeed in the Network Science PhD program.
Research Roundtable events feature presentations in a friendly and collaborative environment, typically given by two postdocs within the Institute. PhD students are encouraged to ask any and all questions about the research process—from original idea to published paper. Presenters discuss a project they have completed, one they are looking for feedback or collaboration on, or anything else! Food and drinks are provided by the Network Science Graduate Student Association and the Northeastern Graduate Student Government.
Network science is an interdisciplinary and thriving area requiring a wide range of knowledge from various fields. Because students come from heterogeneous backgrounds, there is a lot of variation in incoming knowledge, particularly with the many disciplines they will need to master.
In Fall 2017 and 2018, the Network Science Graduate Student Association (NetSI GSA) hosted a bootcamp to kick off the first term. This bootcamp served as an academic orientation, providing a broad overview of topics students need to know. The primary goal is to reduce incoming students’ “unknown unknowns” while providing the tools and resources students need to succeed in the Network Science PhD program.
Research Roundtable events feature presentations in a friendly and collaborative environment, typically given by two postdocs within the Institute. PhD students are encouraged to ask any and all questions about the research process—from original idea to published paper. Presenters discuss a project they have completed, one they are looking for feedback or collaboration on, or anything else! Food and drinks are provided by the Network Science Graduate Student Association and the Northeastern Graduate Student Government.
Network science is an interdisciplinary and thriving area requiring a wide range of knowledge from various fields. Because students come from heterogeneous backgrounds, there is a lot of variation in incoming knowledge, particularly with the many disciplines they will need to master.
In Fall 2017 and 2018, the Network Science Graduate Student Association (NetSI GSA) hosted a bootcamp to kick off the first term. This bootcamp served as an academic orientation, providing a broad overview of topics students need to know. The primary goal is to reduce incoming students’ “unknown unknowns” while providing the tools and resources students need to succeed in the Network Science PhD program.
Research Roundtable events feature presentations in a friendly and collaborative environment, typically given by two postdocs within the Institute. PhD students are encouraged to ask any and all questions about the research process—from original idea to published paper. Presenters discuss a project they have completed, one they are looking for feedback or collaboration on, or anything else! Food and drinks are provided by the Network Science Graduate Student Association and the Northeastern Graduate Student Government.
Network science is an interdisciplinary and thriving area requiring a wide range of knowledge from various fields. Because students come from heterogeneous backgrounds, there is a lot of variation in incoming knowledge, particularly with the many disciplines they will need to master.
In Fall 2017 and 2018, the Network Science Graduate Student Association (NetSI GSA) hosted a bootcamp to kick off the first term. This bootcamp served as an academic orientation, providing a broad overview of topics students need to know. The primary goal is to reduce incoming students’ “unknown unknowns” while providing the tools and resources students need to succeed in the Network Science PhD program.
Research Roundtable events feature presentations in a friendly and collaborative environment, typically given by two postdocs within the Institute. PhD students are encouraged to ask any and all questions about the research process—from original idea to published paper. Presenters discuss a project they have completed, one they are looking for feedback or collaboration on, or anything else! Food and drinks are provided by the Network Science Graduate Student Association and the Northeastern Graduate Student Government.
Shugars, S., Beauchamp, N. (Accepted). Why Keep Arguing? Predicting Participation in Political Conversations Online. SAGE Open: Social Media and Political Participation Global Issue.
Zeng X., Li J., Wang L., Beauchamp N., Shugars S., Wong K.F. (2018) Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse, Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL). Retrieved from http://www.aclweb.org/anthology/N18-1035
Miller J, Shugars, S, Levine, D. (2018). Games for Civic Renewal. The Good Society. Retrieved from https://www.jstor.org/stable/10.5325/goodsociety.26.1.0135
Yucesoy, B., Wang, X., Huang, J., & Barabási, A. (2018). Success in books: A big data approach to bestsellers. EPJ Data Science, 7(1), 1-25. 10.1140/epjds/s13688-018-0135-y
Shi, C., Liu, Y., & Zhang, P. (2018). Weighted community detection and data clustering using message passing. Journal of Statistical Mechanics: Theory and Experiment. 2018 Mar 14;2018(3):033405.
Foley, M., Forber, P., Smead, R., & Riedl, C. (2018). Conflict and convention in dynamic networks. Journal of the Royal Society Interface,15(140), 20170835.
Gallagher, R.J., Reagan, A., Danforth, C.M., & Dodds, P.S. (2018). Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter. PLoS ONE, 13(4): e0195644.
Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018, in press). . In Proceedings of the ACM: Human-Computer Interaction.
Robertson, R. E., Lazer, D., & Wilson, C. (2018). Auditing the personalization and composition of politically-related search engine results pages. In Proceedings of the 2018 World Wide Web Conference on World Wide Web (pp. 955-965). DOI: 10.1145/3178876.3186143
Epstein, R., Robertson, R.E., Lazer, D., & Wilson, C. (2018). Suppressing the search engine manipulation effect (SEME). In Proceedings of the ACM: Human-Computer Interaction, 1(2), Article 42. DOI: 10.1145/3134677
Robertson, R. E., Tran, F., Lewark, L., & Epstein, R. (2018). Estimates of non-heterosexual prevalence: The roles of anonymity and privacy in survey methodology. Archives of Sexual Behavior, 47(4), 1069-1084. DOI: 10.1007/s10508-017-1044-z
Epstein, R., Ho, M., Hyun, S., Le, C., Robertson, R. E., & Stout, D. (2017). A DSM-5-based online mental health referral inventory: A large-scale validation study. Journal of Technology in Human Services, 35(3), 231-246. DOI: 10.1080/15228835.2017.1356800
Epstein, R., Mejia, J., & Robertson, R. E. (2017). The frequency profile: An informative method for graphing the behavior of individuals post hoc or in real time. Behavior Analysis: Research and Practice, 17(1), 55-73. DOI: 10.1037/bar0000052
Gallagher, R. J., Reing, K., Kale, D., & Steeg, G. V. (2017). Anchored correlation explanation: Topic modeling with minimal domain knowledge. Transactions of the Association for Computational Linguistics, 5, 529-542.
Hassan, M. K., Islam, L., & Haque, S. A. (2017). Degree distribution, rank-size distribution, and leadership persistence in mediation-driven attachment networks. Physica A: Statistical Mechanics and its Applications, 469, 23-30. 10.1016/j.physa.2016.11.001
Pilny, A., Poole, M. S., Reichelmann, A., & Klein, B. (2017). A structurational group decision-making perspective on the commons dilemma: Results from an online public goods game. Journal of Applied Communication Research, 45(4), 413. 10.1080/00909882.2017.1355559
Torres, L., Suarez-Serrato, P., & Eliassi-Rad, T. (2018, in review). Graph Distance from the Topological View of Non-backtracking Cycles. Preprint arXiv:1807.09592
Wang, L., Beauchamp, N., Shugars, S., & Qin, K. (2017). Winning on the merits: The joint effects of content and style on debate outcomes.
Haque, S. A., Islam, M. J., Islam, S., & Grégoire, J. (2016). An architecture for client virtualization: A case study. Computer Networks, 100, 75-89. 10.1016/j.comnet.2016.02.020
Haque, S. A., Islam, S., & Gregoire, J. (2015). Virtual P2P client: Accessing P2P applications using virtual terminals. Short paper presented at the 142-144. 10.1109/ICIN.2015.7073821
Shugars, S., Beauchamp, N. (Accepted). Why Keep Arguing? Predicting Participation in Political Conversations Online. SAGE Open: Social Media and Political Participation Global Issue.
Zeng X., Li J., Wang L., Beauchamp N., Shugars S., Wong K.F. (2018) Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse, Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL). Retrieved from http://www.aclweb.org/anthology/N18-1035
Miller J, Shugars, S, Levine, D. (2018). Games for Civic Renewal. The Good Society. Retrieved from https://www.jstor.org/stable/10.5325/goodsociety.26.1.0135
Yucesoy, B., Wang, X., Huang, J., & Barabási, A. (2018). Success in books: A big data approach to bestsellers. EPJ Data Science, 7(1), 1-25. 10.1140/epjds/s13688-018-0135-y
Shi, C., Liu, Y., & Zhang, P. (2018). Weighted community detection and data clustering using message passing. Journal of Statistical Mechanics: Theory and Experiment. 2018 Mar 14;2018(3):033405.
Foley, M., Forber, P., Smead, R., & Riedl, C. (2018). Conflict and convention in dynamic networks. Journal of the Royal Society Interface,15(140), 20170835.
Gallagher, R.J., Reagan, A., Danforth, C.M., & Dodds, P.S. (2018). Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter. PLoS ONE, 13(4): e0195644.
Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018, in press). . In Proceedings of the ACM: Human-Computer Interaction.
Robertson, R. E., Lazer, D., & Wilson, C. (2018). Auditing the personalization and composition of politically-related search engine results pages. In Proceedings of the 2018 World Wide Web Conference on World Wide Web (pp. 955-965). DOI: 10.1145/3178876.3186143
Epstein, R., Robertson, R.E., Lazer, D., & Wilson, C. (2018). Suppressing the search engine manipulation effect (SEME). In Proceedings of the ACM: Human-Computer Interaction, 1(2), Article 42. DOI: 10.1145/3134677
Robertson, R. E., Tran, F., Lewark, L., & Epstein, R. (2018). Estimates of non-heterosexual prevalence: The roles of anonymity and privacy in survey methodology. Archives of Sexual Behavior, 47(4), 1069-1084. DOI: 10.1007/s10508-017-1044-z
Epstein, R., Ho, M., Hyun, S., Le, C., Robertson, R. E., & Stout, D. (2017). A DSM-5-based online mental health referral inventory: A large-scale validation study. Journal of Technology in Human Services, 35(3), 231-246. DOI: 10.1080/15228835.2017.1356800
Epstein, R., Mejia, J., & Robertson, R. E. (2017). The frequency profile: An informative method for graphing the behavior of individuals post hoc or in real time. Behavior Analysis: Research and Practice, 17(1), 55-73. DOI: 10.1037/bar0000052
Gallagher, R. J., Reing, K., Kale, D., & Steeg, G. V. (2017). Anchored correlation explanation: Topic modeling with minimal domain knowledge. Transactions of the Association for Computational Linguistics, 5, 529-542.
Hassan, M. K., Islam, L., & Haque, S. A. (2017). Degree distribution, rank-size distribution, and leadership persistence in mediation-driven attachment networks. Physica A: Statistical Mechanics and its Applications, 469, 23-30. 10.1016/j.physa.2016.11.001
Pilny, A., Poole, M. S., Reichelmann, A., & Klein, B. (2017). A structurational group decision-making perspective on the commons dilemma: Results from an online public goods game. Journal of Applied Communication Research, 45(4), 413. 10.1080/00909882.2017.1355559
Torres, L., Suarez-Serrato, P., & Eliassi-Rad, T. (2018, in review). Graph Distance from the Topological View of Non-backtracking Cycles. Preprint arXiv:1807.09592
Wang, L., Beauchamp, N., Shugars, S., & Qin, K. (2017). Winning on the merits: The joint effects of content and style on debate outcomes.
Haque, S. A., Islam, M. J., Islam, S., & Grégoire, J. (2016). An architecture for client virtualization: A case study. Computer Networks, 100, 75-89. 10.1016/j.comnet.2016.02.020
Haque, S. A., Islam, S., & Gregoire, J. (2015). Virtual P2P client: Accessing P2P applications using virtual terminals. Short paper presented at the 142-144. 10.1109/ICIN.2015.7073821
Shugars, S., Beauchamp, N. (Accepted). Why Keep Arguing? Predicting Participation in Political Conversations Online. SAGE Open: Social Media and Political Participation Global Issue.
Zeng X., Li J., Wang L., Beauchamp N., Shugars S., Wong K.F. (2018) Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse, Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL). Retrieved from http://www.aclweb.org/anthology/N18-1035
Miller J, Shugars, S, Levine, D. (2018). Games for Civic Renewal. The Good Society. Retrieved from https://www.jstor.org/stable/10.5325/goodsociety.26.1.0135
Yucesoy, B., Wang, X., Huang, J., & Barabási, A. (2018). Success in books: A big data approach to bestsellers. EPJ Data Science, 7(1), 1-25. 10.1140/epjds/s13688-018-0135-y
Shi, C., Liu, Y., & Zhang, P. (2018). Weighted community detection and data clustering using message passing. Journal of Statistical Mechanics: Theory and Experiment. 2018 Mar 14;2018(3):033405.
Foley, M., Forber, P., Smead, R., & Riedl, C. (2018). Conflict and convention in dynamic networks. Journal of the Royal Society Interface,15(140), 20170835.
Gallagher, R.J., Reagan, A., Danforth, C.M., & Dodds, P.S. (2018). Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter. PLoS ONE, 13(4): e0195644.
Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018, in press). . In Proceedings of the ACM: Human-Computer Interaction.
Robertson, R. E., Lazer, D., & Wilson, C. (2018). Auditing the personalization and composition of politically-related search engine results pages. In Proceedings of the 2018 World Wide Web Conference on World Wide Web (pp. 955-965). DOI: 10.1145/3178876.3186143
Epstein, R., Robertson, R.E., Lazer, D., & Wilson, C. (2018). Suppressing the search engine manipulation effect (SEME). In Proceedings of the ACM: Human-Computer Interaction, 1(2), Article 42. DOI: 10.1145/3134677
Robertson, R. E., Tran, F., Lewark, L., & Epstein, R. (2018). Estimates of non-heterosexual prevalence: The roles of anonymity and privacy in survey methodology. Archives of Sexual Behavior, 47(4), 1069-1084. DOI: 10.1007/s10508-017-1044-z
Epstein, R., Ho, M., Hyun, S., Le, C., Robertson, R. E., & Stout, D. (2017). A DSM-5-based online mental health referral inventory: A large-scale validation study. Journal of Technology in Human Services, 35(3), 231-246. DOI: 10.1080/15228835.2017.1356800
Epstein, R., Mejia, J., & Robertson, R. E. (2017). The frequency profile: An informative method for graphing the behavior of individuals post hoc or in real time. Behavior Analysis: Research and Practice, 17(1), 55-73. DOI: 10.1037/bar0000052
Gallagher, R. J., Reing, K., Kale, D., & Steeg, G. V. (2017). Anchored correlation explanation: Topic modeling with minimal domain knowledge. Transactions of the Association for Computational Linguistics, 5, 529-542.
Hassan, M. K., Islam, L., & Haque, S. A. (2017). Degree distribution, rank-size distribution, and leadership persistence in mediation-driven attachment networks. Physica A: Statistical Mechanics and its Applications, 469, 23-30. 10.1016/j.physa.2016.11.001
Pilny, A., Poole, M. S., Reichelmann, A., & Klein, B. (2017). A structurational group decision-making perspective on the commons dilemma: Results from an online public goods game. Journal of Applied Communication Research, 45(4), 413. 10.1080/00909882.2017.1355559
Torres, L., Suarez-Serrato, P., & Eliassi-Rad, T. (2018, in review). Graph Distance from the Topological View of Non-backtracking Cycles. Preprint arXiv:1807.09592
Wang, L., Beauchamp, N., Shugars, S., & Qin, K. (2017). Winning on the merits: The joint effects of content and style on debate outcomes.
Haque, S. A., Islam, M. J., Islam, S., & Grégoire, J. (2016). An architecture for client virtualization: A case study. Computer Networks, 100, 75-89. 10.1016/j.comnet.2016.02.020
Haque, S. A., Islam, S., & Gregoire, J. (2015). Virtual P2P client: Accessing P2P applications using virtual terminals. Short paper presented at the 142-144. 10.1109/ICIN.2015.7073821
Shugars, S., Beauchamp, N. (Accepted). Why Keep Arguing? Predicting Participation in Political Conversations Online. SAGE Open: Social Media and Political Participation Global Issue.
Zeng X., Li J., Wang L., Beauchamp N., Shugars S., Wong K.F. (2018) Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse, Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL). Retrieved from http://www.aclweb.org/anthology/N18-1035
Miller J, Shugars, S, Levine, D. (2018). Games for Civic Renewal. The Good Society. Retrieved from https://www.jstor.org/stable/10.5325/goodsociety.26.1.0135
Yucesoy, B., Wang, X., Huang, J., & Barabási, A. (2018). Success in books: A big data approach to bestsellers. EPJ Data Science, 7(1), 1-25. 10.1140/epjds/s13688-018-0135-y
Shi, C., Liu, Y., & Zhang, P. (2018). Weighted community detection and data clustering using message passing. Journal of Statistical Mechanics: Theory and Experiment. 2018 Mar 14;2018(3):033405.
Foley, M., Forber, P., Smead, R., & Riedl, C. (2018). Conflict and convention in dynamic networks. Journal of the Royal Society Interface,15(140), 20170835.
Gallagher, R.J., Reagan, A., Danforth, C.M., & Dodds, P.S. (2018). Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter. PLoS ONE, 13(4): e0195644.
Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018, in press). . In Proceedings of the ACM: Human-Computer Interaction.
Robertson, R. E., Lazer, D., & Wilson, C. (2018). Auditing the personalization and composition of politically-related search engine results pages. In Proceedings of the 2018 World Wide Web Conference on World Wide Web (pp. 955-965). DOI: 10.1145/3178876.3186143
Epstein, R., Robertson, R.E., Lazer, D., & Wilson, C. (2018). Suppressing the search engine manipulation effect (SEME). In Proceedings of the ACM: Human-Computer Interaction, 1(2), Article 42. DOI: 10.1145/3134677
Robertson, R. E., Tran, F., Lewark, L., & Epstein, R. (2018). Estimates of non-heterosexual prevalence: The roles of anonymity and privacy in survey methodology. Archives of Sexual Behavior, 47(4), 1069-1084. DOI: 10.1007/s10508-017-1044-z
Epstein, R., Ho, M., Hyun, S., Le, C., Robertson, R. E., & Stout, D. (2017). A DSM-5-based online mental health referral inventory: A large-scale validation study. Journal of Technology in Human Services, 35(3), 231-246. DOI: 10.1080/15228835.2017.1356800
Epstein, R., Mejia, J., & Robertson, R. E. (2017). The frequency profile: An informative method for graphing the behavior of individuals post hoc or in real time. Behavior Analysis: Research and Practice, 17(1), 55-73. DOI: 10.1037/bar0000052
Gallagher, R. J., Reing, K., Kale, D., & Steeg, G. V. (2017). Anchored correlation explanation: Topic modeling with minimal domain knowledge. Transactions of the Association for Computational Linguistics, 5, 529-542.
Hassan, M. K., Islam, L., & Haque, S. A. (2017). Degree distribution, rank-size distribution, and leadership persistence in mediation-driven attachment networks. Physica A: Statistical Mechanics and its Applications, 469, 23-30. 10.1016/j.physa.2016.11.001
Pilny, A., Poole, M. S., Reichelmann, A., & Klein, B. (2017). A structurational group decision-making perspective on the commons dilemma: Results from an online public goods game. Journal of Applied Communication Research, 45(4), 413. 10.1080/00909882.2017.1355559
Torres, L., Suarez-Serrato, P., & Eliassi-Rad, T. (2018, in review). Graph Distance from the Topological View of Non-backtracking Cycles. Preprint arXiv:1807.09592
Wang, L., Beauchamp, N., Shugars, S., & Qin, K. (2017). Winning on the merits: The joint effects of content and style on debate outcomes.
Haque, S. A., Islam, M. J., Islam, S., & Grégoire, J. (2016). An architecture for client virtualization: A case study. Computer Networks, 100, 75-89. 10.1016/j.comnet.2016.02.020
Haque, S. A., Islam, S., & Gregoire, J. (2015). Virtual P2P client: Accessing P2P applications using virtual terminals. Short paper presented at the 142-144. 10.1109/ICIN.2015.7073821
