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Boleslaw Szymanski - Rensselaer Polytechnic Institute. Troy, NY, US

Boleslaw Szymanski Boleslaw Szymanski

Claire & Roland Schmitt Distinguished Professor, Computer Science | Rensselaer Polytechnic Institute


Specializes in network science with a current focus on social and computer networks


Areas of Expertise (7)

Computer Wireless

Computation & Information Technology

Computer Networks

Network Science

Energy and the Environment

Social Networks

Sensor Networks


Dr. Boleslaw K. Szymanski is the Claire and Roland Schmitt Distinguished Professor at the Department of Computer Science and the Director of the ARL Social and Cognitive Networks Academic Research Center at the Rensselaer Polytechnic Institute. He received his Ph.D. in Computer Science from National Academy of Sciences in Warsaw, Poland, in 1976. Dr. Szymanski published over four hundred scientific articles. He is a foreign member of the National Academy of Science in Poland, an IEEE Fellow and a member of the IEEE Computer Society, and Association for Computing Machinery for which he was National Lecturer. He received the Wiley Distinguished Faculty Award in 2003 and the Wilkes Medal of British Computer Society in 2009. His research interests cover the broad area of network science with current focus on social and computer networks.






Boleslaw Szymanski


Education (2)

Polish Academy of Sciences: Ph.D., Computer Science 1976

Warsaw University of Technology: M.Sc., Electronics 1973

Media Appearances (4)

When It Comes to Politics, Americans Are Divided. Can Data Change That?

Fortune  online


As the ideological gaps between Democrats and Republicans appear to grow increasingly wider, there is now new data that shows what could be driving that political divide. “We tend to think humans behave unpredictably, but more and more we see that, in a lot of settings, human choices can be explained by abstract and elegant models,” said Boleslaw Szymanski, a computer science professor at Rensselaer Polytechnic Institute.

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Weeks before Midterms, Independents Vastly Outnumber Party Members

IVN News  online


In the latest Gallup poll taken from August 1-12, 2018, independents outnumbered members of either major party by at least 15 percentage points, but politicians continue to tailor their messages for the most extreme wings of a polarized two-party system.

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How Innovators Choose Their Next Career Move

Kellogg Insight  online


What do online financial services, commercial spacecraft, and mass-market electric cars all have in common? Other than being industries shaped by serial entrepreneur Elon Musk, not a whole lot.

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Key to Swaying Mass Opinion Found

Live Science  online


For an opinion or belief, 10 percent is critical mass. If that proportion of the population emphatically embraces an idea, then it will spread rapidly to the majority of the population, scientists have found.

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Articles (3)

Influence Maximization for Fixed Heterogeneous Thresholds

Scientific Reports

Panagiotis D Karampourniotis, Boleslaw K Szymanski, Gyorgy Korniss

2019 Influence Maximization is a NP-hard problem of selecting the optimal set of influencers in a network. Here, we propose two new approaches to influence maximization based on two very different metrics. The first metric, termed Balanced Index (BI), is fast to compute and assigns top values to two kinds of nodes: those with high resistance to adoption, and those with large out-degree. This is done by linearly combining three properties of a node: its degree, susceptibility to new opinions, and the impact its activation will have on its neighborhood. Controlling the weights between those three terms has a huge impact on performance. The second metric, termed Group Performance Index (GPI), measures performance of each node as an initiator when it is a part of randomly selected initiator set. In each such selection, the score assigned to each teammate is inversely proportional to the number of initiators causing the desired spread. These two metrics are applicable to various cascade models; here we test them on the Linear Threshold Model with fixed and known thresholds. Furthermore, we study the impact of network degree assortativity and threshold distribution on the cascade size for metrics including ours. The results demonstrate our two metrics deliver strong performance for influence maximization.

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Predicting complex user behavior from CDR based social networks


Casey Doyle, Zala Herga, Stephen Dipple, Boleslaw K. Szymanski, Gyorgy Korniss, Dunja Mladenic

2019 Call Detail Record (CDR) datasets provide enough information about personal interactions to support building and analyzing detailed empirical social networks. We take one such dataset and describe the various ways of using it to create a true social network in spite of the highly noisy data source. We use the resulting network to predict each individual's likelihood to default on payments for the network services, a complex behavior that involves a combination of social, economic, and legal considerations. We use a large number of features extracted from the network to build a model for predicting which users will default. By analyzing the relative contributions of features, we choose their best performing subsets ranging in size from small to medium. Features based on the number of close ties maintained by a user performed better than those derived from user's geographical location. The paper contributions include systematic impact analysis that the number of calls cutoff has on the properties of the network derived from CDR, and a methodology for building complex behavior models by creating very large sets of diverse features and systematically choosing those which perform best for the final model.

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Regularized Stochastic Block Model for robust community detection in complex networks


Xiaoyan Lu, Boleslaw K. Szymanski

2019 The stochastic block model is able to generate different network partitions, ranging from traditional assortative communities to disassortative structures. Since the degree-corrected stochastic block model does not specify which mixing pattern is desired, the inference algorithms, which discover the most likely partition of the networks nodes, are likely to get trapped in the local optima of the log-likelihood. Here we introduce a new model constraining nodes' internal degrees ratios in the objective function to stabilize the inference of block models from the observed network data. Given the regularized model, the inference algorithms, such as Markov chain Monte Carlo, reliably finds assortative or disassortive structure as directed by the value of a single parameter. We show experimentally that the inference of our proposed model quickly converges to the desired assortative or disassortative partition while the inference of degree-corrected stochastic block model gets often trapped at the inferior local optimal partitions when the traditional assortative community structure is not strong in the observed networks.

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