• Amelkin V., Singh, A.K. "Fighting Opinion Control in Social Networks via Link Recommendation", in Proc. of ACM Conference on Knowledge Discovery and Data Mining (KDD), 2019
  
  [abstract], [pdf], [doi], [supplement], [poster], [slide], [], [bibtex]  |  [acceptance rate: 14.2%]

  Existing socio-psychological studies show that the process of opinion formation is inherently a network process, with user opinions in a social network being attracted to a certain average opinion. One simple and intuitive incarnation of this notion of an opinion attractor is the average π^Tx of user opinions x weighted by the users' eigenvector centralities π. This value is a lucrative target for control, as altering it essentially changes the mass opinion in the network. Since any potentially malicious influence upon the opinion distribution in a society is undesirable, it is important to design methods to prevent external attacks upon it.

  In this work, we assume that the adversary aims to maliciously change the network's average opinion by altering the opinions of some unknown users. We, then, state an NP-hard problem—DIVER—of disabling such network opinion control attempts via strategically altering the network's users' eigenvector centralities by recommending a limited number of links to the users. Relying on Markov chain theory, we provide perturbation analysis that shows how eigencentrality and, hence, our problem's objective change in response to a link's addition to the network. The latter leads to the design of a pseudo-linear-time heuristic, relying on efficient estimation of mean first passage times in Markov chains. We have confirmed our theoretical and algorithmic findings, and studied effectiveness and efficiency of our heuristic in experiments with synthetic and real-world networks.

  @inproceedings{amelkin2019fighting,
      author = {Amelkin, Victor and Singh, Ambuj K.},
      title = {Fighting Opinion Control in Social Networks via Link Recommendation},
      booktitle={Proc. of ACM SIGKDD Conference of Knowledge Discovery and Data Mining (KDD'19)},
      year = {2019},
      pages = {677-685},
      organization = {ACM},
      month = aug,
      days = {4--8},
      address = {Anchorage, AK, US},
      doi = {10.1145/3292500.3330960}
  }
  

• Amelkin V., Bogdanov P., Singh A.K. "A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks" (Extended Paper), ACM Trans. on Knowledge Discovery from Data (TKDD), 13(4), 2019
  
  [abstract], [pdf], [doi], [slides], [code], [bibtex]

  Analysis of opinion dynamics in social networks plays an important role in today's life. For predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing polar opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network evolved abnormally? Furthermore, can we predict how the opinions of the users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions can spread beyond users' ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously and capture the connection between the individuals' behavior and the global evolution pattern of the social network.

  In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen model of polar opinion dynamics. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users and, as such, is applicable to large-scale online social networks. In our experiments with synthetic and Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. The same predictions presented in precision-recall space show that SND retains perfect precision for recall up to 0.2. Its precision then decreases while maintaining more than 2-fold improvement over alternatives for recall up to 0.95. When used for opinion prediction in Twitter data, SND's accuracy is 75.6%, which is 7.5% higher than that of the next best method.

  @article{amelkin2019distance,
      author = {Amelkin, Victor and Bogdanov, Petko and Singh, Ambuj K.},
      title = {A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks},
      journal = {ACM Transactions on Knowledge Discovery from Data (TKDD)},
      volume = {13},
      number = {4},
      year = {2019},
      month = aug,
      publisher = {ACM},
      doi = {10.1145/3332168}
  } 
  

• Amelkin V., Askarisichani O., Kim Y.J., Malone T.W., Singh A.K. "Dynamics of Collective Performance in Collaboration Networks", PLOS ONE 13(10), 2018
  
  [abstract], [pdf], [doi], [data], [supplement], [bibtex]

  Today, many complex tasks are assigned to teams, rather than individuals. One reason for teaming up is expansion of the skill coverage of each individual to the joint team skill set. However, numerous empirical studies of human groups suggest that the performance of equally skilled teams can widely differ. Two natural question arise: What are the factors defining team performance? and How can we best predict the performance of a given team on a specific task? While the team members' task-related capabilities constrain the potential for the team's success, the key to understanding team performance is in the analysis of the team process, encompassing the behaviors of the team members during task completion.

  In this study, we extend the existing body of research on team process and prediction models of team performance. Specifically, we analyze the dynamics of historical team performance over a series of tasks as well as the fine-grained patterns of collaboration between team members, and formally connect these dynamics to the team performance in the predictive models. Our major qualitative finding is that higher performing teams have well-connected collaboration networks—as indicated by the topological and spectral properties of the latter—which are more robust to perturbations, and where network processes spread more efficiently. Our major quantitative finding is that our predictive models deliver accurate team performance predictions—with a relative prediction error of 15-25%—on a variety of simple tasks, outperforming baseline models that do not capture the micro-level dynamics of team member behaviors. We also show how to use our models in an application, for the purposes of optimal online planning of workload distribution in an organization. Our findings emphasize the importance of studying the dynamics of team collaboration as the major driver of high performance in teams.

  @article{amelkin2018dynamics,
      title={Dynamics of Collective Performance in Collaboration Networks},
      author={Amelkin, Victor and Askarisichani, Omid and Kim, Young Ji and Malone, Thomas W. and Singh, Ambuj K.},
      journal={PLOS ONE},
      year={2018},
      volume={13},
      issue={10},
      publisher={PLOS}
  }
  

• Amelkin V., Bullo F., Singh A.K. "Polar Opinion Dynamics in Social Networks", IEEE Transactions on Automatic Control (TAC), 62(11), 2017
  
  [abstract], [pdf], [doi], [poster], [slides], [bibtex]

  For decades, scientists have studied opinion formation in social networks, where information travels via word of mouth. The particularly interesting case is when polar opinions—Democrats vs. Republicans or iOS vs. Android—compete in the network. The central problem is to design and analyze a model that captures how polar opinions evolve in the real world.

  In this work, we propose a general non-linear model of polar opinion dynamics, rooted in several theories of sociology and social psychology. The model’s key distinguishing trait is that, unlike in the existing linear models, such as DeGroot and Friedkin-Johnsen models, an individual’s susceptibility to persuasion is a function of his or her current opinion. For example, a person holding a neutral opinion may be rather malleable, while “extremists” may be strongly committed to their current beliefs. We also study three specializations of our general model, whose susceptibility functions correspond to different socio-psychological theories.

  We provide a comprehensive theoretical analysis of our nonlinear models’ behavior using several tools from non-smooth analysis of dynamical systems. To study convergence, we use non-smooth max-min Lyapunov functions together with the generalized Invariance Principle. For our general model, we derive a general sufficient condition for the convergence to consensus. For the specialized models, we provide a full theoretical analysis of their convergence—whether to consensus or disagreement. Our results are rather general and easily apply to the analysis of other non-linear models defined over directed networks, with Lyapunov functions constructed out of convex components.

  @article{amelkin2017polar,
      title={Polar Opinion Dynamics in Social Networks},
      author={Amelkin, Victor and Bullo, Francesco and Singh, Ambuj K.},
      journal={IEEE Transactions on Automatic Control},
      year={2017},
      volume={62},
      issue={11},
      pages={5650-5665},
      publisher={IEEE},
      doi={10.1109/TAC.2017.2694341}
  }
  

• Amelkin V., Bogdanov P., Singh A.K. "A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks", In Proc. of IEEE Int. Conf. on Data Engineering (ICDE), 2017
  
  [abstract], [pdf], [doi], [full paper] [poster], [slides], [code], [bibtex]  |  [acceptance rate: 17.7%]
  Modeling and predicting people's opinions plays an important role in today's life. For viral marketing and political strategy design, it is particularly important to be able to analyze competing opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network "behaved" abnormally? Furthermore, can we predict how the opinions of individual users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions spread beyond users' ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously. In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen opinion dynamics model. SND has a rich semantics of a transportation problem, yet, is computable in pseudo-linear time, thereby, being applicable to large-scale social networks analysis. We demonstrate the effectiveness of SND in experiments with Twitter data.

  @inproceedings{amelkin2017distance,
      title={A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks},
      author={Amelkin, Victor and Bogdanov, Petko and Singh, Ambuj K.},
      booktitle={Proc. International Conference on Data Engineering (ICDE)},
      pages={159--162},
      year={2017},
      organization={IEEE}
  }
  

• Amelkin, V., Venkatesh S., Vohra R. "Contagion and Equilibria in Diversified Financial Networks", (in submission, Dec 2021)
  
  [abstract], [pdf], [ssrn], [bibtex]

  Diversified cross-shareholding networks are thought to be more resilient to shocks, but diversification also increases the channels by which a shock can spread. To resolve these competing intuitions we introduce a stochastic model of a diversified cross-shareholding network in which a firm's valuation depends on its cash endowment and the shares it owns in other firms.

  We show that a concentration of measure phenomenon emerges: almost all realized network instances drawn from any probability distribution in a wide class are resilient to contagion if endowments are sufficiently large. Furthermore, the size of a shock needed to trigger widespread default increases with the exposure of firms to each other. Distributions in this class are characterized by the property that a firm's equity shares owned by others are weakly dependent yet lack "dominant" shareholders.

  @article{amelkin2021contagion,
      title={Contagion and Equilibria in Diversified Financial Networks},
      author={Amelkin, Victor and Venkatesh, Santosh and Vohra, Rakesh},
      year={2021},
      month=dec,
      journal={PIER Working Paper No. 21-029}
      note={Available at SSRN \url{http://dx.doi.org/10.2139/ssrn.3974645}}
  }
  

• Amelkin V., Vohra R. "Strategic Formation and Reliability of Supply Chain Networks", arXiv:1909.08021 [cs.GT] (submitted, Sep 17, 2019; revised, Jan 10, 2020)
  
  [abstract], [pdf], [arxiv], [slides], [bibtex]

  Supply chains are the backbone of the global economy. Disruptions to them can be costly. Centrally managed supply chains invest in ensuring their resilience. Decentralized supply chains, however, must rely upon the self-interest of their individual components to maintain the resilience of the entire chain.

  We examine the incentives that independent self-interested agents have in forming a resilient supply chain network in the face of production disruptions and competition. In our model, competing suppliers are subject to yield uncertainty (they deliver less than ordered) and congestion (lead time uncertainty or, "soft" supply caps). Competing retailers must decide which suppliers to link to based on both price and reliability.

  In the presence of yield uncertainty only, the resulting supply chain networks are sparse. Retailers concentrate their links on a single supplier, counter to the idea that they should mitigate yield uncertainty by diversifying their supply base. This happens because retailers benefit from supply variance. It suggests that competition will amplify output uncertainty. When congestion is included as well, the resulting networks are denser and resemble the bipartite expander graphs that have been proposed in the supply chain literature, thereby, providing the first example of endogenous formation of resilient supply chain networks, without resilience being explicitly encoded in payoffs. Finally, we show that a suppliers investments in improved yield can make them worse off. This happens because high production output saturates the market, which, in turn lowers prices and profits for participants.

  @article{amelkin2019strategic,
      title={Strategic Formation and Reliability of Supply Chain Networks},
      author={Amelkin, Victor and Vohra, Rakesh},
      journal={arXiv:1909.08021 [cs.GT]},
      year={2020},
      month=jan,
      note={Available at \url{https://arxiv.org/abs/1909.08021}}
  }
  

• Amelkin V., Vohra R. "Yield Uncertainty and Strategic Formation of Supply Chain Networks", arXiv:1907.09943 [cs.GT] (submitted, Dec 2021)
  
  [abstract], [pdf], [arxiv], [bibtex]
  How does supply uncertainty affect the structure of supply chain networks? To answer this question we consider a setting where retailers and suppliers must establish a costly relationship with each other prior to engaging in trade. Suppliers, with uncertain yield, announce wholesale prices, while retailers must decide which suppliers to link to based on their wholesale prices. Subsequently, retailers compete with each other in Cournot fashion to sell the acquired supply to consumers. We find that in (pure strategy Nash) equilibrium, retailers concentrate their links among too few suppliers, i.e., there is insufficient diversification of the supply base. We find that either reduction of supply variance or increase of mean supply, increases a supplier's profit. However, these two ways of improving service have qualitatively different effects on welfare: improvement of the expected supply by a supplier makes everyone better off, whereas improvement of supply variance lowers consumer surplus.

  @article{amelkin2019yield,
      title={Yield Uncertainty and Strategic Formation of Supply Chain Networks},
      author={Amelkin, Victor and Vohra, Rakesh},
      journal={arXiv:1907.09943 [cs.GT]},
      year={2019},
      month=jul,
      note={Available at \url{https://arxiv.org/abs/1907.09943}}
  }
  

• Linear Algebra and Graphs, Spectral Clustering (grad.; Fall, 2015; Network Science IGERT Bootcamp)
• Foundations of Computer Science (ugrad.; Summer, 2015; Spring-Summer, 2013)
• Object-Oriented Design and Implementation in C++ (ugrad.; Summer, 2013; Fall, 2012)
• Numerical Simulation (grad.; Winter, 2013)

I have served as a reviewer for a number of computer science, engineering, economics, and sociology conferences, journals, and publishers, including KDD'18'17'16'15, WWW'18'17, SDM'17'16, ICDM'16'13, WSDM'16, SIGMOD'14, TKDD'20, TKDE'18'17'15'14, TNSE'18'17, DMKD'19, AAAI'17'16; TAC'20'19'18, Automatica'20'19'18, CDC'19'17, ACC'19'18, ECC'19, Scientific Reports'18; International Economic Review'19; Journal of Mathematical Sociology'20'19'18; and Cambridge University Press'18.

I serve on the Editorial Board of The Journal of Mathematical Sociology ('18-…).

I used to organize the weekly Theory Seminar at the University of Pennsylvania ('18-'20).

• social-network-distance — a distance measure for the comparison of a social network's snapshots containing polar (competing) opinions. It measures how "likely" one polar opinion distribution over a social network has evolved into another opinion distribution with respect to a given opinion dynamics model. Implemented in MATLAB.
• matlab-transport — a fast transportation problem solver for MATLAB, based on Andrew Goldberg's CS2 implementation of Goldberg-Tarjan's min-cost network flow algorithm. It works much faster than MATLAB's linprog or CPLEX' general-purpose LP-solver.
• matlab-sssp — multiple MATLAB implementations of Dijkstra's single-source shortest path algorithm for sparse networks: the binary heap-based implementation, due to David Bindel; the radix heap-based implementation; and the implementation based on an improved version of Dial's algorithm.
• cnp-1.6-nix — a GNU/Linux and macOS port of Complex Network Package 1.6 — a graph library for MATLAB.