Modeling of Network Processes

Graphs have evolved into a rich framework for the solution of diverse problems, ranging from mathematical puzzles to the analysis of planetary-scale social networks. A particular class of problems that has attracted recent interest in the research community is related to how to model information that is embedded in a graph structure. In this scenario, the graph dynamics is guided by processes that are localized both in time and space, with effects at different scales. For instance, traffic in road networks and content popularity on the Web are two scenarios where understanding the role played by network processes (e.g. traffic jams, information diffusion) might lead to new models, algorithms, and data structures for managing large dynamic graphs. We are developing methods for modeling and analyzing network processes using ideas from graph theory, information theory, and sampling. 

Selected Publications

Affiliated Faculty

Research interests: 

Machine learning and data mining, combinatorial algorithms, linear algebra, dynamical systems, and their application to the analysis, modeling, and control of dynamic processes in networks, such as online social networks and collaboration networks.


Victor graduated with a PhD in Computer Science in June, 2018, having worked on network processes with an emphasis on social, collaboration, and economic networks. On July 1, 2018, he joined the University of Pennsylvania as a postdoc. Prior to working at Dynamo, Victor worked in scientific computing, and spent several years as a software engineer in industry. He obtained a MSc degree in Applied Mathematics and Computer Science from Tula State University, Russia in 2008.

Research interests: 

Data science, network science, machine learning, brain networks

Haraldur recieved a B.Sc in Electrical Engineering and a B.Sc in Computer Science at the University of Iceland in 2013 and 2014, respectively. His Ph.D. research is focused on statistical and machine learning methods to model and understand the human brain from MRI data. Prior to joining Dynamo in 2014 he developed methods to diagnose sleep disorders, and since joining Haraldur has studied deep learning architectures at Google Research and the effect social networks have on physical behavior at Evidation Health, among other projects.

Photo of Zexi Huang.
Research interests: 

Network Mining, Representation Learning, Graph Signal Processing, Transfer Learning

Zexi received his B.Eng. in Computer Science and Technology at University of Electronic Science and Technology of China, Chengdu in 2018. He joined Dynamo lab in 2018. During his bachelor, he also worked as a visiting research assistant at Nanyang Technological University, Singapore.

Sikun Lin
Research interests: 

data mining, machine learning

Sikun received her B.Sc degree in Physics and Math from Hong Kong University of Science and Technology in 2015 and her MPhil degree in Computer Science from the same university in 2017.

Research interests: 

Graph-based machine learning and their applications. Network interactions. Dynamic networks.

Wei received his Dr.rer.nat (PhD) degree in Computer Science from Lugwig-Maximilians University of Munich in 2018. Before joining the DYNAMO lab, he worked as a researcher in the Department of AI Platform, Tencent.